The Low-light Image Enhancement Method Based on Improved LSID Algorithm

Abstract

Aiming at the problem of insufficient exposure of images obtained by photography and photography in realistic low-light, backlight and other scenarios, this paper proposes a low-light image enhancement deep learning network model that improved the Learning-to-See-In-the-Dark (LSID) algorithm. This method implements the amplification factor estimation weight network by design. The defects of the previous artificially designed parameter amplification factors were compared, and through the training and testing of experimental data, the experimental results of different methods were compared in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural SIMilarity (SSIM) parameter indicators, and image visualization was compared at the same time. The experimental results show that the proposed method in this paper can improve the PSNR and SSIM indicators by 0.81 and 0.025, respectively, and it can obtain better image dark light enhancement effect.