Color Channel Fusion Network For Low-Light Image Enhancement

Abstract

When capturing images in low light condition, due to insufficient lighting, the true color information and texture details of objects are difficult to obtain. Considering that, we propose an end-to-end color channel fusion network (CCFN). Specifically, our proposed method uses partial channel combination inputs to obtain multiple enhancement results. The relevance among RGB channels is maintained by modeling channel interdependencies. Subsequently, a multi-scale feature channel shuffle module (MFCS) is designed to combine image features at different scales, which makes the fusion images hold more rich information. Finally, the output images are generated after detail enhancement. Extensive experiments demonstrate the superiority of our method over several state-of-the-arts in terms of enhancement quality.