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Low-light image enhancement is generally regarded as a challenging task inimage processing, especially for the complex visual tasks at night or weaklyilluminated. In order to reduce the blurs or noises on the low-light images, alarge number of papers have contributed to applying different technologies.Regretfully, most of them had served little purposes in coping with theextremely poor illumination parts of images or test in practice. In this work,the authors propose a novel approach for processing low-light images based onthe Retinex theory and generative adversarial network (GAN), which is composedof the decomposition part for splitting the image into illumination image andreflected image, and the enhancement part for generating high-quality image.Such a discriminative network is expected to make the generated image clearer.Couples of experiments have been implemented under the circumstance ofdifferent lighting strength on the basis of Converted See-In-the-Dark (CSID)datasets, and the satisfactory results have been achieved with exceedingexpectation that much encourages the authors. In a word, the proposed GAN-basednetwork and employed Retinex theory in this work have proven to be effective indealing with the low-light image enhancement problems, which will benefit theimage processing with no doubt.

Low-light Image Enhancement Algorithm Based on Retinex and Generative Adversarial Network