Images captured in low light conditions are unsuitable for human and machine vision due to low brightness and sharpness and high noise, and have a negative effect on their performance. Much research has been done to improve such images. The methods proposed so far to so
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Images captured in low light conditions are unsuitable for human and machine vision due to low brightness and sharpness and high noise, and have a negative effect on their performance. Much research has been done to improve such images. The methods proposed so far to solve this problem greatly improve such images. One of these methods is the RETINEX-based method, which modifies low-light images, but because the initial structure of this method is complex and inefficient, researchers have developed other methods such as SSR, MSR, and MSRCR. To solve the problem, they have presented this approach. These methods, in turn, have problems such as abnormal images and amplification of noise. In the continuation of the work done, the field of optimization has been used, which shows better performance than the previous works. In this research, by obtaining the optimal brightness component, using nonlinear conversion and applying smoothing filter and reducing noise on the image as a post-processing step, these weaknesses are largely eliminated. By applying the proposed method, the resulting images look more natural and their information is more preserved. Subjective and objective criteria such as EI, SSIM, PSNR and IMMSE were used to evaluate the proposed method. The simulation results show the superiority of the proposed method over the competing methods.
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