LEGAN: New Dark and Weak Light Image Enhancement Algorithm

Abstract

In order to solve the problems of low brightness, contrast, signal-to-noise ratio and high noise pollution in dark and weak light images, this paper proposes a new dark and weak light image enhancement algorithm LEGAN (low-light enhancement generative adversarial network). This algorithm first inputs the image into the proposed Gamma curve estimation network to obtain the feature map containing Gamma parameters, then enhances the brightness through LEB (light enhancement block) module, and iteratively enhances the result by cascading LEB. Then, a global-local discriminator structure based on PatchGAN is used to improve image resolution and recover details. Finally, the gap between the true label and the output result is limited by introducing a perceived loss. The lighting smoothness loss is used to maintain the monotonic relationship between adjacent pixels. Meanwhile, the spatial consistency loss is combined to enhance the spatial correlation of images. Experimental results show that compared with most of the current mainstream enhancement algorithms, the proposed algorithm has a higher degree of detail restoration, and can effectively avoid the problem of ill illumination in the local region of enhanced image.

Keywords

• dark and weak light; image enhancement; gamma curve estimation network; global-local discriminator; loss function