Better Than Reference In Low Light Image Enhancement: Conditional Re-Enhancement Networks

TL;DR

This paper introduces CRENet, a data-driven conditional re-enhancement network that improves low light images by simultaneously enhancing contrast, brightness, and reducing noise, outperforming traditional methods and even surpassing reference images.

Contribution

The paper proposes a novel CRENet that leverages the V channel in HSV space for effective low light image enhancement, combining supervised learning with existing models without requiring carefully paired training data.

Findings

CRENet significantly improves image quality in low light conditions.

The method can outperform reference images in contrast and brightness.

Processing time is less than 20 ms for a 400x600 image.

Abstract

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)