Low Light Image Enhancement Algorithm Based on Retinex Model Learning

Low-light images have low contrast and unclear details, resulting in the reduction of available information for human vision. The current mainstream enhancement algorithms have problems such as noise amplification, color distortion, and dependence on data sets during the enhancement process. Therefore, a low-light image enhancement algorithm based on Retinex model and deep learning is proposed. First, the proposed residual network cascade is applied to the decomposition network based on Retinex theory to improve the gradient disappearance problem of the deep neural network, and at the same time obtain the illumination and reflection components of the image. Secondly, an adaptive gamma transformation function is designed to enhance the illumination component, which can effectively improve the low contrast problem of the image; since the noise in the dark area is amplified during the enhancement process, a full-scale learning network is designed to improve the image quality. The reflection component is denoised. Finally, the enhanced illumination component and reflection component are fused to obtain the final enhanced image. In the data set test results, the peak signal-to-noise ratio of the proposed algorithm is improved by an average of 0.33dB compared with the mainstream algorithm Zero-DCE, and by an average of 0.48dB compared with the URetinex-Net algorithm. The experimental results show that the algorithm can effectively improve the brightness of the image while reducing the image noise and accurately restore the texture information of the image.