Efficient Global Color, Luminance, and Contrast Consistency Optimization for Multiple Remote Sensing Images
Light and color uniformity is essential for the production of high-quality remote-sensing image mosaics. Existing color correction methods mainly use flexible models to express the color differences between multiple images and impose specific constraints (e.g., image gradient or contrast constraints...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9987645/ |
| _version_ | 1828086233719373824 |
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| author | Zhonghua Hong Changyou Xu Xiaohua Tong Shijie Liu Ruyan Zhou Haiyan Pan Yun Zhang Yanling Han Jing Wang Shuhu Yang |
| author_facet | Zhonghua Hong Changyou Xu Xiaohua Tong Shijie Liu Ruyan Zhou Haiyan Pan Yun Zhang Yanling Han Jing Wang Shuhu Yang |
| author_sort | Zhonghua Hong |
| collection | DOAJ |
| description | Light and color uniformity is essential for the production of high-quality remote-sensing image mosaics. Existing color correction methods mainly use flexible models to express the color differences between multiple images and impose specific constraints (e.g., image gradient or contrast constraints) to preserve image texture information as much as possible. Due to these constraints, it is usually difficult to correct for the differences in texture between images during image processing. We propose a method that can optimize the luminance, contrast, and color difference of remote-sensing images. In the YCbCr color space, this method processes the chrominance and luminance channels of the image. This is conducive to reducing the influence of the different channels. In the luminance channel, the block-based Wallis transform method is used to optimize the luminance and contrast of the image. In the chromaticity channel, to optimize the color differences, a spline curve is used as a model; the color differences are formulated as a cost function and solved using convex quadratic programming. Moreover, considering the efficiency of our method, we use a graphics processing unit to make the algorithm parallel. The proposed method has been tested on several challenging datasets that cover different topographic regions. In terms of visuals and quality indicators, it shows better results than state-of-the-art approaches. |
| first_indexed | 2024-04-11T04:53:47Z |
| format | Article |
| id | doaj.art-e330c9dbf2504ae48585b75e15655bc7 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-04-11T04:53:47Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-e330c9dbf2504ae48585b75e15655bc72022-12-27T00:00:11ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352023-01-011662263710.1109/JSTARS.2022.32293929987645Efficient Global Color, Luminance, and Contrast Consistency Optimization for Multiple Remote Sensing ImagesZhonghua Hong0https://orcid.org/0000-0003-0045-1066Changyou Xu1Xiaohua Tong2https://orcid.org/0000-0002-1045-3797Shijie Liu3https://orcid.org/0000-0002-5941-0763Ruyan Zhou4Haiyan Pan5Yun Zhang6Yanling Han7Jing Wang8Shuhu Yang9https://orcid.org/0000-0001-9967-7756College of Information Technology, Shanghai Ocean University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics, Tongji University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai, ChinaLight and color uniformity is essential for the production of high-quality remote-sensing image mosaics. Existing color correction methods mainly use flexible models to express the color differences between multiple images and impose specific constraints (e.g., image gradient or contrast constraints) to preserve image texture information as much as possible. Due to these constraints, it is usually difficult to correct for the differences in texture between images during image processing. We propose a method that can optimize the luminance, contrast, and color difference of remote-sensing images. In the YCbCr color space, this method processes the chrominance and luminance channels of the image. This is conducive to reducing the influence of the different channels. In the luminance channel, the block-based Wallis transform method is used to optimize the luminance and contrast of the image. In the chromaticity channel, to optimize the color differences, a spline curve is used as a model; the color differences are formulated as a cost function and solved using convex quadratic programming. Moreover, considering the efficiency of our method, we use a graphics processing unit to make the algorithm parallel. The proposed method has been tested on several challenging datasets that cover different topographic regions. In terms of visuals and quality indicators, it shows better results than state-of-the-art approaches.https://ieeexplore.ieee.org/document/9987645/Color consistencycontrast optimizationluminance correction |
| spellingShingle | Zhonghua Hong Changyou Xu Xiaohua Tong Shijie Liu Ruyan Zhou Haiyan Pan Yun Zhang Yanling Han Jing Wang Shuhu Yang Efficient Global Color, Luminance, and Contrast Consistency Optimization for Multiple Remote Sensing Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Color consistency contrast optimization luminance correction |
| title | Efficient Global Color, Luminance, and Contrast Consistency Optimization for Multiple Remote Sensing Images |
| title_full | Efficient Global Color, Luminance, and Contrast Consistency Optimization for Multiple Remote Sensing Images |
| title_fullStr | Efficient Global Color, Luminance, and Contrast Consistency Optimization for Multiple Remote Sensing Images |
| title_full_unstemmed | Efficient Global Color, Luminance, and Contrast Consistency Optimization for Multiple Remote Sensing Images |
| title_short | Efficient Global Color, Luminance, and Contrast Consistency Optimization for Multiple Remote Sensing Images |
| title_sort | efficient global color luminance and contrast consistency optimization for multiple remote sensing images |
| topic | Color consistency contrast optimization luminance correction |
| url | https://ieeexplore.ieee.org/document/9987645/ |
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