A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation

Abstract Recently, learning-based image interpolation methods have gained significant popularity in the field of surveillance image processing due to their promising results. Notably, deep neural networks have shown considerable improvements in image super-resolution. To enhance the performance of image interpolation for surveillance images, researchers often employ deep convolutional neural Networks. However, merely increasing the network depth may not lead to substantial improvements and could even introduce new training-related challenges, necessitating novel training approaches. Therefore, in this proposed work, a new deep learning-based model is developed specifically tailored for effective surveillance image interpolation. The approach begins with the Optimized Recursive Least Square Adaptive Filter (ORLSAF) technique for image filtering. This step involves calculating the error signal and estimating weight factors to generate noise-free images. To reconstruct the interpolated surveillance images, the innovative Multi-Variate Dense Fusion Network (MVDFN) methodology is utilized, which incorporates feature fusion, augmentation, and loss regularization processes. Particularly, the loss factor is optimally calculated using the Hybrid Butterfly Optimization (HBO) algorithm. To evaluate the performance of the proposed technique, extensive experiments are conducted using a benchmarking dataset commonly employed in surveillance image processing. The evaluation metrics used include Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), loss factor, and normalized similarity index. Overall, this research aims to advance surveillance image interpolation using a novel deep learning-based approach, combining ORLSAF, MVDFN, and the HBO algorithm to achieve superior results compared to existing methods. The potential impact of this work includes enhancing the quality and clarity of surveillance images, contributing to improved surveillance systems and applications. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Diana Earshia, V. [verfasserIn] Sumathi, M.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Image interpolation Reconstruction Optimized recursive least square adaptive filter (ORLSAF) Multi-variate dense fusion network (MVDFN) Hybrid butterfly optimization (HBO) Super resolution (SR)

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Signal, image and video processing - London [u.a.] : Springer, 2007, 18(2023), 2 vom: 25. Okt., Seite 991-1005
Übergeordnetes Werk:	volume:18 ; year:2023 ; number:2 ; day:25 ; month:10 ; pages:991-1005

Links:	Volltext

DOI / URN:	10.1007/s11760-023-02805-7

Katalog-ID:	SPR054836239

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520			\|a Abstract Recently, learning-based image interpolation methods have gained significant popularity in the field of surveillance image processing due to their promising results. Notably, deep neural networks have shown considerable improvements in image super-resolution. To enhance the performance of image interpolation for surveillance images, researchers often employ deep convolutional neural Networks. However, merely increasing the network depth may not lead to substantial improvements and could even introduce new training-related challenges, necessitating novel training approaches. Therefore, in this proposed work, a new deep learning-based model is developed specifically tailored for effective surveillance image interpolation. The approach begins with the Optimized Recursive Least Square Adaptive Filter (ORLSAF) technique for image filtering. This step involves calculating the error signal and estimating weight factors to generate noise-free images. To reconstruct the interpolated surveillance images, the innovative Multi-Variate Dense Fusion Network (MVDFN) methodology is utilized, which incorporates feature fusion, augmentation, and loss regularization processes. Particularly, the loss factor is optimally calculated using the Hybrid Butterfly Optimization (HBO) algorithm. To evaluate the performance of the proposed technique, extensive experiments are conducted using a benchmarking dataset commonly employed in surveillance image processing. The evaluation metrics used include Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), loss factor, and normalized similarity index. Overall, this research aims to advance surveillance image interpolation using a novel deep learning-based approach, combining ORLSAF, MVDFN, and the HBO algorithm to achieve superior results compared to existing methods. The potential impact of this work includes enhancing the quality and clarity of surveillance images, contributing to improved surveillance systems and applications.
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allfields	10.1007/s11760-023-02805-7 doi (DE-627)SPR054836239 (SPR)s11760-023-02805-7-e DE-627 ger DE-627 rakwb eng Diana Earshia, V. verfasserin aut A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Recently, learning-based image interpolation methods have gained significant popularity in the field of surveillance image processing due to their promising results. Notably, deep neural networks have shown considerable improvements in image super-resolution. To enhance the performance of image interpolation for surveillance images, researchers often employ deep convolutional neural Networks. However, merely increasing the network depth may not lead to substantial improvements and could even introduce new training-related challenges, necessitating novel training approaches. Therefore, in this proposed work, a new deep learning-based model is developed specifically tailored for effective surveillance image interpolation. The approach begins with the Optimized Recursive Least Square Adaptive Filter (ORLSAF) technique for image filtering. This step involves calculating the error signal and estimating weight factors to generate noise-free images. To reconstruct the interpolated surveillance images, the innovative Multi-Variate Dense Fusion Network (MVDFN) methodology is utilized, which incorporates feature fusion, augmentation, and loss regularization processes. Particularly, the loss factor is optimally calculated using the Hybrid Butterfly Optimization (HBO) algorithm. To evaluate the performance of the proposed technique, extensive experiments are conducted using a benchmarking dataset commonly employed in surveillance image processing. The evaluation metrics used include Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), loss factor, and normalized similarity index. Overall, this research aims to advance surveillance image interpolation using a novel deep learning-based approach, combining ORLSAF, MVDFN, and the HBO algorithm to achieve superior results compared to existing methods. The potential impact of this work includes enhancing the quality and clarity of surveillance images, contributing to improved surveillance systems and applications. Image interpolation (dpeaa)DE-He213 Reconstruction (dpeaa)DE-He213 Optimized recursive least square adaptive filter (ORLSAF) (dpeaa)DE-He213 Multi-variate dense fusion network (MVDFN) (dpeaa)DE-He213 Hybrid butterfly optimization (HBO) (dpeaa)DE-He213 Super resolution (SR) (dpeaa)DE-He213 Sumathi, M. aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 2 vom: 25. Okt., Seite 991-1005 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:2 day:25 month:10 pages:991-1005 https://dx.doi.org/10.1007/s11760-023-02805-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 2 25 10 991-1005
spelling	10.1007/s11760-023-02805-7 doi (DE-627)SPR054836239 (SPR)s11760-023-02805-7-e DE-627 ger DE-627 rakwb eng Diana Earshia, V. verfasserin aut A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Recently, learning-based image interpolation methods have gained significant popularity in the field of surveillance image processing due to their promising results. Notably, deep neural networks have shown considerable improvements in image super-resolution. To enhance the performance of image interpolation for surveillance images, researchers often employ deep convolutional neural Networks. However, merely increasing the network depth may not lead to substantial improvements and could even introduce new training-related challenges, necessitating novel training approaches. Therefore, in this proposed work, a new deep learning-based model is developed specifically tailored for effective surveillance image interpolation. The approach begins with the Optimized Recursive Least Square Adaptive Filter (ORLSAF) technique for image filtering. This step involves calculating the error signal and estimating weight factors to generate noise-free images. To reconstruct the interpolated surveillance images, the innovative Multi-Variate Dense Fusion Network (MVDFN) methodology is utilized, which incorporates feature fusion, augmentation, and loss regularization processes. Particularly, the loss factor is optimally calculated using the Hybrid Butterfly Optimization (HBO) algorithm. To evaluate the performance of the proposed technique, extensive experiments are conducted using a benchmarking dataset commonly employed in surveillance image processing. The evaluation metrics used include Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), loss factor, and normalized similarity index. Overall, this research aims to advance surveillance image interpolation using a novel deep learning-based approach, combining ORLSAF, MVDFN, and the HBO algorithm to achieve superior results compared to existing methods. The potential impact of this work includes enhancing the quality and clarity of surveillance images, contributing to improved surveillance systems and applications. Image interpolation (dpeaa)DE-He213 Reconstruction (dpeaa)DE-He213 Optimized recursive least square adaptive filter (ORLSAF) (dpeaa)DE-He213 Multi-variate dense fusion network (MVDFN) (dpeaa)DE-He213 Hybrid butterfly optimization (HBO) (dpeaa)DE-He213 Super resolution (SR) (dpeaa)DE-He213 Sumathi, M. aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 2 vom: 25. Okt., Seite 991-1005 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:2 day:25 month:10 pages:991-1005 https://dx.doi.org/10.1007/s11760-023-02805-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 2 25 10 991-1005
allfields_unstemmed	10.1007/s11760-023-02805-7 doi (DE-627)SPR054836239 (SPR)s11760-023-02805-7-e DE-627 ger DE-627 rakwb eng Diana Earshia, V. verfasserin aut A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Recently, learning-based image interpolation methods have gained significant popularity in the field of surveillance image processing due to their promising results. Notably, deep neural networks have shown considerable improvements in image super-resolution. To enhance the performance of image interpolation for surveillance images, researchers often employ deep convolutional neural Networks. However, merely increasing the network depth may not lead to substantial improvements and could even introduce new training-related challenges, necessitating novel training approaches. Therefore, in this proposed work, a new deep learning-based model is developed specifically tailored for effective surveillance image interpolation. The approach begins with the Optimized Recursive Least Square Adaptive Filter (ORLSAF) technique for image filtering. This step involves calculating the error signal and estimating weight factors to generate noise-free images. To reconstruct the interpolated surveillance images, the innovative Multi-Variate Dense Fusion Network (MVDFN) methodology is utilized, which incorporates feature fusion, augmentation, and loss regularization processes. Particularly, the loss factor is optimally calculated using the Hybrid Butterfly Optimization (HBO) algorithm. To evaluate the performance of the proposed technique, extensive experiments are conducted using a benchmarking dataset commonly employed in surveillance image processing. The evaluation metrics used include Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), loss factor, and normalized similarity index. Overall, this research aims to advance surveillance image interpolation using a novel deep learning-based approach, combining ORLSAF, MVDFN, and the HBO algorithm to achieve superior results compared to existing methods. The potential impact of this work includes enhancing the quality and clarity of surveillance images, contributing to improved surveillance systems and applications. Image interpolation (dpeaa)DE-He213 Reconstruction (dpeaa)DE-He213 Optimized recursive least square adaptive filter (ORLSAF) (dpeaa)DE-He213 Multi-variate dense fusion network (MVDFN) (dpeaa)DE-He213 Hybrid butterfly optimization (HBO) (dpeaa)DE-He213 Super resolution (SR) (dpeaa)DE-He213 Sumathi, M. aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 2 vom: 25. Okt., Seite 991-1005 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:2 day:25 month:10 pages:991-1005 https://dx.doi.org/10.1007/s11760-023-02805-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 2 25 10 991-1005
allfieldsGer	10.1007/s11760-023-02805-7 doi (DE-627)SPR054836239 (SPR)s11760-023-02805-7-e DE-627 ger DE-627 rakwb eng Diana Earshia, V. verfasserin aut A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Recently, learning-based image interpolation methods have gained significant popularity in the field of surveillance image processing due to their promising results. Notably, deep neural networks have shown considerable improvements in image super-resolution. To enhance the performance of image interpolation for surveillance images, researchers often employ deep convolutional neural Networks. However, merely increasing the network depth may not lead to substantial improvements and could even introduce new training-related challenges, necessitating novel training approaches. Therefore, in this proposed work, a new deep learning-based model is developed specifically tailored for effective surveillance image interpolation. The approach begins with the Optimized Recursive Least Square Adaptive Filter (ORLSAF) technique for image filtering. This step involves calculating the error signal and estimating weight factors to generate noise-free images. To reconstruct the interpolated surveillance images, the innovative Multi-Variate Dense Fusion Network (MVDFN) methodology is utilized, which incorporates feature fusion, augmentation, and loss regularization processes. Particularly, the loss factor is optimally calculated using the Hybrid Butterfly Optimization (HBO) algorithm. To evaluate the performance of the proposed technique, extensive experiments are conducted using a benchmarking dataset commonly employed in surveillance image processing. The evaluation metrics used include Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), loss factor, and normalized similarity index. Overall, this research aims to advance surveillance image interpolation using a novel deep learning-based approach, combining ORLSAF, MVDFN, and the HBO algorithm to achieve superior results compared to existing methods. The potential impact of this work includes enhancing the quality and clarity of surveillance images, contributing to improved surveillance systems and applications. Image interpolation (dpeaa)DE-He213 Reconstruction (dpeaa)DE-He213 Optimized recursive least square adaptive filter (ORLSAF) (dpeaa)DE-He213 Multi-variate dense fusion network (MVDFN) (dpeaa)DE-He213 Hybrid butterfly optimization (HBO) (dpeaa)DE-He213 Super resolution (SR) (dpeaa)DE-He213 Sumathi, M. aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 2 vom: 25. Okt., Seite 991-1005 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:2 day:25 month:10 pages:991-1005 https://dx.doi.org/10.1007/s11760-023-02805-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 2 25 10 991-1005
allfieldsSound	10.1007/s11760-023-02805-7 doi (DE-627)SPR054836239 (SPR)s11760-023-02805-7-e DE-627 ger DE-627 rakwb eng Diana Earshia, V. verfasserin aut A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Recently, learning-based image interpolation methods have gained significant popularity in the field of surveillance image processing due to their promising results. Notably, deep neural networks have shown considerable improvements in image super-resolution. To enhance the performance of image interpolation for surveillance images, researchers often employ deep convolutional neural Networks. However, merely increasing the network depth may not lead to substantial improvements and could even introduce new training-related challenges, necessitating novel training approaches. Therefore, in this proposed work, a new deep learning-based model is developed specifically tailored for effective surveillance image interpolation. The approach begins with the Optimized Recursive Least Square Adaptive Filter (ORLSAF) technique for image filtering. This step involves calculating the error signal and estimating weight factors to generate noise-free images. To reconstruct the interpolated surveillance images, the innovative Multi-Variate Dense Fusion Network (MVDFN) methodology is utilized, which incorporates feature fusion, augmentation, and loss regularization processes. Particularly, the loss factor is optimally calculated using the Hybrid Butterfly Optimization (HBO) algorithm. To evaluate the performance of the proposed technique, extensive experiments are conducted using a benchmarking dataset commonly employed in surveillance image processing. The evaluation metrics used include Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), loss factor, and normalized similarity index. Overall, this research aims to advance surveillance image interpolation using a novel deep learning-based approach, combining ORLSAF, MVDFN, and the HBO algorithm to achieve superior results compared to existing methods. The potential impact of this work includes enhancing the quality and clarity of surveillance images, contributing to improved surveillance systems and applications. Image interpolation (dpeaa)DE-He213 Reconstruction (dpeaa)DE-He213 Optimized recursive least square adaptive filter (ORLSAF) (dpeaa)DE-He213 Multi-variate dense fusion network (MVDFN) (dpeaa)DE-He213 Hybrid butterfly optimization (HBO) (dpeaa)DE-He213 Super resolution (SR) (dpeaa)DE-He213 Sumathi, M. aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 2 vom: 25. Okt., Seite 991-1005 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:2 day:25 month:10 pages:991-1005 https://dx.doi.org/10.1007/s11760-023-02805-7 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 2 25 10 991-1005
language	English
source	Enthalten in Signal, image and video processing 18(2023), 2 vom: 25. Okt., Seite 991-1005 volume:18 year:2023 number:2 day:25 month:10 pages:991-1005
sourceStr	Enthalten in Signal, image and video processing 18(2023), 2 vom: 25. Okt., Seite 991-1005 volume:18 year:2023 number:2 day:25 month:10 pages:991-1005
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Image interpolation Reconstruction Optimized recursive least square adaptive filter (ORLSAF) Multi-variate dense fusion network (MVDFN) Hybrid butterfly optimization (HBO) Super resolution (SR)
isfreeaccess_bool	false
container_title	Signal, image and video processing
authorswithroles_txt_mv	Diana Earshia, V. @@aut@@ Sumathi, M. @@aut@@
publishDateDaySort_date	2023-10-25T00:00:00Z
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author	Diana Earshia, V.
spellingShingle	Diana Earshia, V. misc Image interpolation misc Reconstruction misc Optimized recursive least square adaptive filter (ORLSAF) misc Multi-variate dense fusion network (MVDFN) misc Hybrid butterfly optimization (HBO) misc Super resolution (SR) A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation
authorStr	Diana Earshia, V.
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topic_title	A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation Image interpolation (dpeaa)DE-He213 Reconstruction (dpeaa)DE-He213 Optimized recursive least square adaptive filter (ORLSAF) (dpeaa)DE-He213 Multi-variate dense fusion network (MVDFN) (dpeaa)DE-He213 Hybrid butterfly optimization (HBO) (dpeaa)DE-He213 Super resolution (SR) (dpeaa)DE-He213
topic	misc Image interpolation misc Reconstruction misc Optimized recursive least square adaptive filter (ORLSAF) misc Multi-variate dense fusion network (MVDFN) misc Hybrid butterfly optimization (HBO) misc Super resolution (SR)
topic_unstemmed	misc Image interpolation misc Reconstruction misc Optimized recursive least square adaptive filter (ORLSAF) misc Multi-variate dense fusion network (MVDFN) misc Hybrid butterfly optimization (HBO) misc Super resolution (SR)
topic_browse	misc Image interpolation misc Reconstruction misc Optimized recursive least square adaptive filter (ORLSAF) misc Multi-variate dense fusion network (MVDFN) misc Hybrid butterfly optimization (HBO) misc Super resolution (SR)
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title	A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation
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title_full	A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation
author_sort	Diana Earshia, V.
journal	Signal, image and video processing
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author_browse	Diana Earshia, V. Sumathi, M.
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author-letter	Diana Earshia, V.
doi_str_mv	10.1007/s11760-023-02805-7
title_sort	guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation
title_auth	A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation
abstract	Abstract Recently, learning-based image interpolation methods have gained significant popularity in the field of surveillance image processing due to their promising results. Notably, deep neural networks have shown considerable improvements in image super-resolution. To enhance the performance of image interpolation for surveillance images, researchers often employ deep convolutional neural Networks. However, merely increasing the network depth may not lead to substantial improvements and could even introduce new training-related challenges, necessitating novel training approaches. Therefore, in this proposed work, a new deep learning-based model is developed specifically tailored for effective surveillance image interpolation. The approach begins with the Optimized Recursive Least Square Adaptive Filter (ORLSAF) technique for image filtering. This step involves calculating the error signal and estimating weight factors to generate noise-free images. To reconstruct the interpolated surveillance images, the innovative Multi-Variate Dense Fusion Network (MVDFN) methodology is utilized, which incorporates feature fusion, augmentation, and loss regularization processes. Particularly, the loss factor is optimally calculated using the Hybrid Butterfly Optimization (HBO) algorithm. To evaluate the performance of the proposed technique, extensive experiments are conducted using a benchmarking dataset commonly employed in surveillance image processing. The evaluation metrics used include Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), loss factor, and normalized similarity index. Overall, this research aims to advance surveillance image interpolation using a novel deep learning-based approach, combining ORLSAF, MVDFN, and the HBO algorithm to achieve superior results compared to existing methods. The potential impact of this work includes enhancing the quality and clarity of surveillance images, contributing to improved surveillance systems and applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Recently, learning-based image interpolation methods have gained significant popularity in the field of surveillance image processing due to their promising results. Notably, deep neural networks have shown considerable improvements in image super-resolution. To enhance the performance of image interpolation for surveillance images, researchers often employ deep convolutional neural Networks. However, merely increasing the network depth may not lead to substantial improvements and could even introduce new training-related challenges, necessitating novel training approaches. Therefore, in this proposed work, a new deep learning-based model is developed specifically tailored for effective surveillance image interpolation. The approach begins with the Optimized Recursive Least Square Adaptive Filter (ORLSAF) technique for image filtering. This step involves calculating the error signal and estimating weight factors to generate noise-free images. To reconstruct the interpolated surveillance images, the innovative Multi-Variate Dense Fusion Network (MVDFN) methodology is utilized, which incorporates feature fusion, augmentation, and loss regularization processes. Particularly, the loss factor is optimally calculated using the Hybrid Butterfly Optimization (HBO) algorithm. To evaluate the performance of the proposed technique, extensive experiments are conducted using a benchmarking dataset commonly employed in surveillance image processing. The evaluation metrics used include Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), loss factor, and normalized similarity index. Overall, this research aims to advance surveillance image interpolation using a novel deep learning-based approach, combining ORLSAF, MVDFN, and the HBO algorithm to achieve superior results compared to existing methods. The potential impact of this work includes enhancing the quality and clarity of surveillance images, contributing to improved surveillance systems and applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Recently, learning-based image interpolation methods have gained significant popularity in the field of surveillance image processing due to their promising results. Notably, deep neural networks have shown considerable improvements in image super-resolution. To enhance the performance of image interpolation for surveillance images, researchers often employ deep convolutional neural Networks. However, merely increasing the network depth may not lead to substantial improvements and could even introduce new training-related challenges, necessitating novel training approaches. Therefore, in this proposed work, a new deep learning-based model is developed specifically tailored for effective surveillance image interpolation. The approach begins with the Optimized Recursive Least Square Adaptive Filter (ORLSAF) technique for image filtering. This step involves calculating the error signal and estimating weight factors to generate noise-free images. To reconstruct the interpolated surveillance images, the innovative Multi-Variate Dense Fusion Network (MVDFN) methodology is utilized, which incorporates feature fusion, augmentation, and loss regularization processes. Particularly, the loss factor is optimally calculated using the Hybrid Butterfly Optimization (HBO) algorithm. To evaluate the performance of the proposed technique, extensive experiments are conducted using a benchmarking dataset commonly employed in surveillance image processing. The evaluation metrics used include Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), loss factor, and normalized similarity index. Overall, this research aims to advance surveillance image interpolation using a novel deep learning-based approach, combining ORLSAF, MVDFN, and the HBO algorithm to achieve superior results compared to existing methods. The potential impact of this work includes enhancing the quality and clarity of surveillance images, contributing to improved surveillance systems and applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation
url	https://dx.doi.org/10.1007/s11760-023-02805-7
remote_bool	true
author2	Sumathi, M.
author2Str	Sumathi, M.
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doi_str	10.1007/s11760-023-02805-7
up_date	2024-07-04T03:12:20.797Z
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A guided optimized recursive least square adaptive filtering based multi-variate dense fusion network model for image interpolation

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