GDNet: a low-light image enhancement network based on Ghost-Block and unique image decomposition

Rui Chang, Gang Liu, Yao Qian, Haojie Tang, Gaoqiang Wang, Durga Prasad Bavirisetti

Abstract: In this paper, we explore an algorithm for low-light image enhancement based on Retinex modules. A low light enhancement network based on Ghost-Block and unique image decomposition is proposed, termed as GDNet. This addresses the problem of color distortion that exists in current enhancement algorithms. Firstly, a special module called Ghost-Block is proposed which can effectively reduce the redundant features in the network. Secondly, we design a unique decomposition network based on the Ghost-Block. It decomposes low-light image into texture map, color map, and illumination map, representing the texture structure, color, and illumination distribution in the original image, respectively. Taking into account the influence of different illumination scenarios on color information in the subsequent adjustment process, we develop a coupled color and illumination adjustment network. This treats color and illumination adjustment in low-light enhancement as a joint optimization problem rather than separate sub-tasks, aiming to achieve a natural color distribution with balanced illumination in the enhanced image. Finally, considering potential loss of texture detail information during the light adjustment process, we design a texture adjustment network to further restore the texture structure in natural lighting scenes. Extensive experiments demonstrate that our algorithm outperforms state-of-the-art methods in terms of light adjustment and color fidelity. Moreover, our proposed algorithm exhibits a high degree of similarity with Ground Truth in experiments using normal lighting images as a test set. The source code will be released at https://github.com/xpp0429/GDNet.