Hybrid Image Enhancement With Progressive Laplacian Enhancing Unit

In this paper, we propose a novel hybrid network with Laplacian enhancing unit for image enhancement. We combine the merits of two representative enhancement methods, i.e., the scaling scheme and the generative scheme, by forming a hybrid enhancing module. Meanwhile, we model image enhancement in a progressive manner with a deep cascading CNN architecture, in which the previous feature maps are used to enhance subsequent features to get an improved performance. Specifically, we propose a Laplacian enhancing unit, which can adjustably enhance the detail information by adding the residual of previous feature maps. This unit is embedded across layers for progressively enhancing the features. We build our network on the U-Net architecture and name it Hybrid Progressive Enhancing U-Net. Experiments show that our method achieves superior image enhancement results compared with the state-of-the-arts, while retaining competitive implementation efficiency.