FuzzyCIE: fuzzy colour image enhancement for low-exposure images
Abstract Colour image enhancement not only is of high importance in consumer electronics, but also plays significant role in medical imaging, remotely sensed imaging, etc. Moreover, low-exposure colour images inherently lack sufficient image details which are exclusively necessary for workings in th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Mandal, Soham [verfasserIn] Mitra, Sushmita [verfasserIn] Shankar, B. Uma [verfasserIn] |
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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Enthalten in: Soft Computing - Springer-Verlag, 2003, 24(2019), 3 vom: 15. Mai, Seite 2151-2167 |
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| Übergeordnetes Werk: |
volume:24 ; year:2019 ; number:3 ; day:15 ; month:05 ; pages:2151-2167 |
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10.1007/s00500-019-04048-6 |
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| 520 | |a Abstract Colour image enhancement not only is of high importance in consumer electronics, but also plays significant role in medical imaging, remotely sensed imaging, etc. Moreover, low-exposure colour images inherently lack sufficient image details which are exclusively necessary for workings in these domains. To address this less explored problem, a novel enhancement algorithm involving estimation of the fuzzy histogram with thresholding based on the computed effect of exposure value has been proposed. The algorithm operates on the lightness (%$L^{*}%$) component of the input image in %$L^{*}a^{*}b^{*}%$ colour space, while preserving the colour-opponent dimensions (%$a^{*} %$ and %$ b^{*}%$) to maintain the natural outlook of the image. This technique has been experimentally demonstrated over a dataset consisting of images generated at different exposure levels. Quantitative and qualitative analysis of the relative performance of the proposed algorithm has been shown with respect to state-of-the-art enhancement algorithms over the %$L^{*}a^{*}b^{*}%$ space. | ||
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10.1007/s00500-019-04048-6 doi (DE-627)SPR006513239 (SPR)s00500-019-04048-6-e DE-627 ger DE-627 rakwb eng Mandal, Soham verfasserin aut FuzzyCIE: fuzzy colour image enhancement for low-exposure images 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Colour image enhancement not only is of high importance in consumer electronics, but also plays significant role in medical imaging, remotely sensed imaging, etc. Moreover, low-exposure colour images inherently lack sufficient image details which are exclusively necessary for workings in these domains. To address this less explored problem, a novel enhancement algorithm involving estimation of the fuzzy histogram with thresholding based on the computed effect of exposure value has been proposed. The algorithm operates on the lightness (%$L^{*}%$) component of the input image in %$L^{*}a^{*}b^{*}%$ colour space, while preserving the colour-opponent dimensions (%$a^{*} %$ and %$ b^{*}%$) to maintain the natural outlook of the image. This technique has been experimentally demonstrated over a dataset consisting of images generated at different exposure levels. Quantitative and qualitative analysis of the relative performance of the proposed algorithm has been shown with respect to state-of-the-art enhancement algorithms over the %$L^{*}a^{*}b^{*}%$ space. Image enhancement (dpeaa)DE-He213 Histogram equalization (dpeaa)DE-He213 Structural similarity index (dpeaa)DE-He213 Feature similarity index (dpeaa)DE-He213 Low-exposure colour image (dpeaa)DE-He213 Fuzzy histogram (dpeaa)DE-He213 Mitra, Sushmita verfasserin aut Shankar, B. Uma verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 3 vom: 15. Mai, Seite 2151-2167 (DE-627)SPR006469531 nnns volume:24 year:2019 number:3 day:15 month:05 pages:2151-2167 https://dx.doi.org/10.1007/s00500-019-04048-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 3 15 05 2151-2167 |
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10.1007/s00500-019-04048-6 doi (DE-627)SPR006513239 (SPR)s00500-019-04048-6-e DE-627 ger DE-627 rakwb eng Mandal, Soham verfasserin aut FuzzyCIE: fuzzy colour image enhancement for low-exposure images 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Colour image enhancement not only is of high importance in consumer electronics, but also plays significant role in medical imaging, remotely sensed imaging, etc. Moreover, low-exposure colour images inherently lack sufficient image details which are exclusively necessary for workings in these domains. To address this less explored problem, a novel enhancement algorithm involving estimation of the fuzzy histogram with thresholding based on the computed effect of exposure value has been proposed. The algorithm operates on the lightness (%$L^{*}%$) component of the input image in %$L^{*}a^{*}b^{*}%$ colour space, while preserving the colour-opponent dimensions (%$a^{*} %$ and %$ b^{*}%$) to maintain the natural outlook of the image. This technique has been experimentally demonstrated over a dataset consisting of images generated at different exposure levels. Quantitative and qualitative analysis of the relative performance of the proposed algorithm has been shown with respect to state-of-the-art enhancement algorithms over the %$L^{*}a^{*}b^{*}%$ space. Image enhancement (dpeaa)DE-He213 Histogram equalization (dpeaa)DE-He213 Structural similarity index (dpeaa)DE-He213 Feature similarity index (dpeaa)DE-He213 Low-exposure colour image (dpeaa)DE-He213 Fuzzy histogram (dpeaa)DE-He213 Mitra, Sushmita verfasserin aut Shankar, B. Uma verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 3 vom: 15. Mai, Seite 2151-2167 (DE-627)SPR006469531 nnns volume:24 year:2019 number:3 day:15 month:05 pages:2151-2167 https://dx.doi.org/10.1007/s00500-019-04048-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 3 15 05 2151-2167 |
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10.1007/s00500-019-04048-6 doi (DE-627)SPR006513239 (SPR)s00500-019-04048-6-e DE-627 ger DE-627 rakwb eng Mandal, Soham verfasserin aut FuzzyCIE: fuzzy colour image enhancement for low-exposure images 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Colour image enhancement not only is of high importance in consumer electronics, but also plays significant role in medical imaging, remotely sensed imaging, etc. Moreover, low-exposure colour images inherently lack sufficient image details which are exclusively necessary for workings in these domains. To address this less explored problem, a novel enhancement algorithm involving estimation of the fuzzy histogram with thresholding based on the computed effect of exposure value has been proposed. The algorithm operates on the lightness (%$L^{*}%$) component of the input image in %$L^{*}a^{*}b^{*}%$ colour space, while preserving the colour-opponent dimensions (%$a^{*} %$ and %$ b^{*}%$) to maintain the natural outlook of the image. This technique has been experimentally demonstrated over a dataset consisting of images generated at different exposure levels. Quantitative and qualitative analysis of the relative performance of the proposed algorithm has been shown with respect to state-of-the-art enhancement algorithms over the %$L^{*}a^{*}b^{*}%$ space. Image enhancement (dpeaa)DE-He213 Histogram equalization (dpeaa)DE-He213 Structural similarity index (dpeaa)DE-He213 Feature similarity index (dpeaa)DE-He213 Low-exposure colour image (dpeaa)DE-He213 Fuzzy histogram (dpeaa)DE-He213 Mitra, Sushmita verfasserin aut Shankar, B. Uma verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 3 vom: 15. Mai, Seite 2151-2167 (DE-627)SPR006469531 nnns volume:24 year:2019 number:3 day:15 month:05 pages:2151-2167 https://dx.doi.org/10.1007/s00500-019-04048-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 3 15 05 2151-2167 |
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10.1007/s00500-019-04048-6 doi (DE-627)SPR006513239 (SPR)s00500-019-04048-6-e DE-627 ger DE-627 rakwb eng Mandal, Soham verfasserin aut FuzzyCIE: fuzzy colour image enhancement for low-exposure images 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Colour image enhancement not only is of high importance in consumer electronics, but also plays significant role in medical imaging, remotely sensed imaging, etc. Moreover, low-exposure colour images inherently lack sufficient image details which are exclusively necessary for workings in these domains. To address this less explored problem, a novel enhancement algorithm involving estimation of the fuzzy histogram with thresholding based on the computed effect of exposure value has been proposed. The algorithm operates on the lightness (%$L^{*}%$) component of the input image in %$L^{*}a^{*}b^{*}%$ colour space, while preserving the colour-opponent dimensions (%$a^{*} %$ and %$ b^{*}%$) to maintain the natural outlook of the image. This technique has been experimentally demonstrated over a dataset consisting of images generated at different exposure levels. Quantitative and qualitative analysis of the relative performance of the proposed algorithm has been shown with respect to state-of-the-art enhancement algorithms over the %$L^{*}a^{*}b^{*}%$ space. Image enhancement (dpeaa)DE-He213 Histogram equalization (dpeaa)DE-He213 Structural similarity index (dpeaa)DE-He213 Feature similarity index (dpeaa)DE-He213 Low-exposure colour image (dpeaa)DE-He213 Fuzzy histogram (dpeaa)DE-He213 Mitra, Sushmita verfasserin aut Shankar, B. Uma verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 3 vom: 15. Mai, Seite 2151-2167 (DE-627)SPR006469531 nnns volume:24 year:2019 number:3 day:15 month:05 pages:2151-2167 https://dx.doi.org/10.1007/s00500-019-04048-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 3 15 05 2151-2167 |
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10.1007/s00500-019-04048-6 doi (DE-627)SPR006513239 (SPR)s00500-019-04048-6-e DE-627 ger DE-627 rakwb eng Mandal, Soham verfasserin aut FuzzyCIE: fuzzy colour image enhancement for low-exposure images 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Colour image enhancement not only is of high importance in consumer electronics, but also plays significant role in medical imaging, remotely sensed imaging, etc. Moreover, low-exposure colour images inherently lack sufficient image details which are exclusively necessary for workings in these domains. To address this less explored problem, a novel enhancement algorithm involving estimation of the fuzzy histogram with thresholding based on the computed effect of exposure value has been proposed. The algorithm operates on the lightness (%$L^{*}%$) component of the input image in %$L^{*}a^{*}b^{*}%$ colour space, while preserving the colour-opponent dimensions (%$a^{*} %$ and %$ b^{*}%$) to maintain the natural outlook of the image. This technique has been experimentally demonstrated over a dataset consisting of images generated at different exposure levels. Quantitative and qualitative analysis of the relative performance of the proposed algorithm has been shown with respect to state-of-the-art enhancement algorithms over the %$L^{*}a^{*}b^{*}%$ space. Image enhancement (dpeaa)DE-He213 Histogram equalization (dpeaa)DE-He213 Structural similarity index (dpeaa)DE-He213 Feature similarity index (dpeaa)DE-He213 Low-exposure colour image (dpeaa)DE-He213 Fuzzy histogram (dpeaa)DE-He213 Mitra, Sushmita verfasserin aut Shankar, B. Uma verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 24(2019), 3 vom: 15. Mai, Seite 2151-2167 (DE-627)SPR006469531 nnns volume:24 year:2019 number:3 day:15 month:05 pages:2151-2167 https://dx.doi.org/10.1007/s00500-019-04048-6 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 24 2019 3 15 05 2151-2167 |
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Abstract Colour image enhancement not only is of high importance in consumer electronics, but also plays significant role in medical imaging, remotely sensed imaging, etc. Moreover, low-exposure colour images inherently lack sufficient image details which are exclusively necessary for workings in these domains. To address this less explored problem, a novel enhancement algorithm involving estimation of the fuzzy histogram with thresholding based on the computed effect of exposure value has been proposed. The algorithm operates on the lightness (%$L^{*}%$) component of the input image in %$L^{*}a^{*}b^{*}%$ colour space, while preserving the colour-opponent dimensions (%$a^{*} %$ and %$ b^{*}%$) to maintain the natural outlook of the image. This technique has been experimentally demonstrated over a dataset consisting of images generated at different exposure levels. Quantitative and qualitative analysis of the relative performance of the proposed algorithm has been shown with respect to state-of-the-art enhancement algorithms over the %$L^{*}a^{*}b^{*}%$ space. |
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Abstract Colour image enhancement not only is of high importance in consumer electronics, but also plays significant role in medical imaging, remotely sensed imaging, etc. Moreover, low-exposure colour images inherently lack sufficient image details which are exclusively necessary for workings in these domains. To address this less explored problem, a novel enhancement algorithm involving estimation of the fuzzy histogram with thresholding based on the computed effect of exposure value has been proposed. The algorithm operates on the lightness (%$L^{*}%$) component of the input image in %$L^{*}a^{*}b^{*}%$ colour space, while preserving the colour-opponent dimensions (%$a^{*} %$ and %$ b^{*}%$) to maintain the natural outlook of the image. This technique has been experimentally demonstrated over a dataset consisting of images generated at different exposure levels. Quantitative and qualitative analysis of the relative performance of the proposed algorithm has been shown with respect to state-of-the-art enhancement algorithms over the %$L^{*}a^{*}b^{*}%$ space. |
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Abstract Colour image enhancement not only is of high importance in consumer electronics, but also plays significant role in medical imaging, remotely sensed imaging, etc. Moreover, low-exposure colour images inherently lack sufficient image details which are exclusively necessary for workings in these domains. To address this less explored problem, a novel enhancement algorithm involving estimation of the fuzzy histogram with thresholding based on the computed effect of exposure value has been proposed. The algorithm operates on the lightness (%$L^{*}%$) component of the input image in %$L^{*}a^{*}b^{*}%$ colour space, while preserving the colour-opponent dimensions (%$a^{*} %$ and %$ b^{*}%$) to maintain the natural outlook of the image. This technique has been experimentally demonstrated over a dataset consisting of images generated at different exposure levels. Quantitative and qualitative analysis of the relative performance of the proposed algorithm has been shown with respect to state-of-the-art enhancement algorithms over the %$L^{*}a^{*}b^{*}%$ space. |
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10.1007/s00500-019-04048-6 |
| up_date |
2024-07-03T23:20:38.710Z |
| _version_ |
1803601931103371264 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR006513239</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20201124002928.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00500-019-04048-6</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006513239</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00500-019-04048-6-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Mandal, Soham</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">FuzzyCIE: fuzzy colour image enhancement for low-exposure images</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Colour image enhancement not only is of high importance in consumer electronics, but also plays significant role in medical imaging, remotely sensed imaging, etc. Moreover, low-exposure colour images inherently lack sufficient image details which are exclusively necessary for workings in these domains. To address this less explored problem, a novel enhancement algorithm involving estimation of the fuzzy histogram with thresholding based on the computed effect of exposure value has been proposed. The algorithm operates on the lightness (%$L^{*}%$) component of the input image in %$L^{*}a^{*}b^{*}%$ colour space, while preserving the colour-opponent dimensions (%$a^{*} %$ and %$ b^{*}%$) to maintain the natural outlook of the image. This technique has been experimentally demonstrated over a dataset consisting of images generated at different exposure levels. 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7.402298 |