MFC-Net: Multi-scale fusion coding network for Image Deblurring

Haiying Xia, Bo Wu, Yumei Tan, Xiaohu Tang, Shuxiang Song

Abstract: The existing image blind deblurring methods mostly adopt the “coarse-to-fine” scheme, which always require a mass of parameters and can not mine the blur information effectively. To tackle the above problems, we design a lightweight multi-scale fusion coding deblurring network (MFC-Net). Specifically, we fuse the multi-resolution features in a single-scale deblurring framework based on Wasserstein generative adversarial network (WGAN). Then we propose a feature fusion module to replace the addition operation in each scale in the skip connection of the encoder-decoder. Besides, we propose a regional attention module to alleviate the inconsistency in non-uniform blurry images and excavate its intrinsic blurry features simultaneously. Plenty of experimental results show that our proposed deblurring model is simple, fast yet robust for image motion deblurring with real-time inference of 10 ms per 720p image, outperforming the state-of-the-art methods in terms of performance-complexity trade-off.