Low-light image enhancement based on improved Retinex-Net

In order to solve the problem of high noise and insufficient feature extraction of Retinex-Net in low-light image enhancement processing, this paper proposes a new network structure. First, the Retinex-Net network was used as the basic model to decompose the input image, and a residual shrinkage network was introduced in the convolutional layer to remove the noise generated during the decomposition process. Then, in order to preserve the details of the image and suppress noise while enhancing the brightness, the enhancement network was divided into three small sub-networks for processing respectively. Finally, the adjusted images were fused to obtain an enhanced image. Compared with the SIRE、LIME、GLADNet、Retinex-Net algorithm, experiments show that the peak signal-to-noise ratio of the images processed by the algorithm in this paper has an avevage increase of 3.48 dB, the mean square error has an avevage increase of 0.082 7, the structural similarity has an avevage increase of 0.146, and the lightness order error has an avevage increase of 271.6.