  Low-light image enhancement is generally regarded as a challenging task in
image processing, especially for the complex visual tasks at night or weakly
illuminated. In order to reduce the blurs or noises on the low-light images, a
large number of papers have contributed to applying different technologies.
Regretfully, most of them had served little purposes in coping with the
extremely poor illumination parts of images or test in practice. In this work,
the authors propose a novel approach for processing low-light images based on
the Retinex theory and generative adversarial network (GAN), which is composed
of the decomposition part for splitting the image into illumination image and
reflected 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 of
different lighting strength on the basis of Converted See-In-the-Dark (CSID)
datasets, and the satisfactory results have been achieved with exceeding
expectation that much encourages the authors. In a word, the proposed GAN-based
network and employed Retinex theory in this work have proven to be effective in
dealing with the low-light image enhancement problems, which will benefit the
image processing with no doubt.
