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14 February 2020 An improved contrast fusion approach in gradient domain for low light level image enhancement
Shuning Yang, Qiong Song, Xin Guo, Yuehuan Wang
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Proceedings Volume 11428, MIPPR 2019: Multispectral Image Acquisition, Processing, and Analysis; 114280M (2020) https://doi.org/10.1117/12.2539316
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China
Abstract
Images often suffer from low visibility under nonuniform illumination, weak luminance and backlight environment. This paper describes a novel approach to improvement the visualization of poor light conditions. Firstly, we raise the global brightness using an adaptive exponent induced function. To enhance the local detail perception, the local contrast is boosted by contrast preserving which utilizes human vision system model. To not bias from original image, we generate the contrast combined original image and global illuminance enhance output in the gradient domain. To reduce artifacts, the guided filter is employed to estimate the local mean illuminance when transform the contrast. The experimental results demonstrate that our proposed method has a pleasant visual effect and low computational complexity than the state of the arts.
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Shuning Yang, Qiong Song, Xin Guo, and Yuehuan Wang "An improved contrast fusion approach in gradient domain for low light level image enhancement", Proc. SPIE 11428, MIPPR 2019: Multispectral Image Acquisition, Processing, and Analysis, 114280M (14 February 2020); https://doi.org/10.1117/12.2539316
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KEYWORDS
Image enhancement
Visualization
RGB color model
Image contrast enhancement
Image processing
Detection and tracking algorithms
Evolutionary algorithms
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