Adaptive RetinexNet and fusion strategy for low-intensity image enhancement

Xuehui Yin; Qiaoyu Chen; Ge Tu

doi:10.1117/12.2673316

14 April 2023 Adaptive RetinexNet and fusion strategy for low-intensity image enhancement

Xuehui Yin, Qiaoyu Chen, Ge Tu

Proceedings Volume 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022); 126130B (2023) https://doi.org/10.1117/12.2673316
Event: International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 2022, Chongqing, China

Abstract

Aiming at the characteristics of low illumination image, such as low brightness, fuzzy detail information and obvious noise, we propose an adaptive RetinexNet low illumination image enhancement method with the fusion strategy. Use the mutual independence of each channel in the HSV color space model to enhance the brightness component; then use the correlation coefficient to make the saturation component adaptively adjust with the brightness component; on the basis of UNet, combine different areas of the illumination enhancement image to carry different levels of Noise, build a reflectivity noise reduction model; a coarse enhanced image is generated through the above three steps; The camera response model is obtained by combining the camera response function and the brightness conversion function, and the optimal exposure rate is found to obtain the exposure rate map; finally, the overexposed image and the coarsely enhanced image are combined with the original low-light image, and the image block decomposition is used for reconstruction and Fusion to get the final low-light enhanced image. The experimental results show that the algorithm we adopt can ensure the texture and details of the image while enhancing the contrast of the image.

Citation Download Citation

Xuehui Yin, Qiaoyu Chen, and Ge Tu "Adaptive RetinexNet and fusion strategy for low-intensity image enhancement", Proc. SPIE 12613, International Conference on Computer Vision, Application, and Algorithm (CVAA 2022), 126130B (14 April 2023); https://doi.org/10.1117/12.2673316

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KEYWORDS

Image enhancement

Image fusion

Light sources and illumination

Image quality

RGB color model

Image processing

Visualization

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