  This paper proposes a new light-weight convolutional neural network (5k
parameters) for non-uniform illumination image enhancement to handle color,
exposure, contrast, noise and artifacts, etc., simultaneously and effectively.
More concretely, the input image is first enhanced using Retinex model from
dual different aspects (enhancing under-exposure and suppressing
over-exposure), respectively. Then, these two enhanced results and the original
image are fused to obtain an image with satisfactory brightness, contrast and
details. Finally, the extra noise and compression artifacts are removed to get
the final result. To train this network, we propose a semi-supervised
retouching solution and construct a new dataset (82k images) contains various
scenes and light conditions. Our model can enhance 0.5 mega-pixel (like
600*800) images in real time (50 fps), which is faster than existing
enhancement methods. Extensive experiments show that our solution is fast and
effective to deal with non-uniform illumination images.
