Low-Light Image Enhancement Algorithm Based on Lime with Pre-Processing and Post-Processing

Due to the low visibility of the low-light image, it is not conducive to human observation and computer vision algorithm. Inspired by the human vision system (HVS), we propose a simple and effective method of low-light image enhancement. In the proposed method, firstly a sampler is used to get the optimal exposure ratio for the camera response model. Then a generator is used to synthesize dual-exposure images that are well-exposed in the regions where the original image is under-exposed. Next, the enhanced image is processed by using a part of Low-light Image Enhancement via Illumination Map Estimation (LIME) algorithm and the weight matrix of two images will be determined when fusing. After that, the combiner is used to get the synthesized image with all pixel well-exposed and finally a post-processing part is added to make the output image perform better. In the post-processing part, the best gray range of the image is adjusted, and the image is denoised and recomposed by using Block Machine 3-Dimensional (BM3D) model. Experiment results show that the proposed method can enhance the low-light images with less visual information distortions when comparing with those of several recent effective methods.