  Low light images suffer from severe noise, low brightness, low contrast, etc.
In previous researches, many image enhancement methods have been proposed, but
few methods can deal with these problems simultaneously. In this paper, to
solve these problems simultaneously, we propose a low light image enhancement
method that can combined with supervised learning and previous HSV (Hue,
Saturation, Value) or Retinex model based image enhancement methods. First, we
analyse the relationship between the HSV color space and the Retinex theory,
and show that the V channel (V channel in HSV color space, equals the maximum
channel in RGB color space) of the enhanced image can well represent the
contrast and brightness enhancement process. Then, a data-driven conditional
re-enhancement network (denoted as CRENet) is proposed. The network takes low
light images as input and the enhanced V channel as condition, then it can
re-enhance the contrast and brightness of the low light image and at the same
time reduce noise and color distortion. It should be noted that during the
training process, any paired images with different exposure time can be used
for training, and there is no need to carefully select the supervised images
which will save a lot. In addition, it takes less than 20 ms to process a color
image with the resolution 400*600 on a 2080Ti GPU. Finally, some comparative
experiments are implemented to prove the effectiveness of the method. The
results show that the method proposed in this paper can significantly improve
the quality of the enhanced image, and by combining with other image contrast
enhancement methods, the final enhancement result can even be better than the
reference image in contrast and brightness. (Code will be available at
https://github.com/hitzhangyu/image-enhancement-with-denoise)
