Multi-scale Residual Low-Pass Filter Network for Image Deblurring

We present a simple and effective Multi-scale Residual Low-Pass Filter Network (MRLPFNet) that jointly explores the image details and main structures for image deblurring. Our work is motivated by an observation that the difference between the blurry image and the clear one not only contains high-frequency contents
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but also includes low-frequency information due to the influence of blur, while using the standard residual learning is less effective for modeling the main structure distorted by the blur. Considering that the low-frequency contents usually correspond to main global structures that are spatially variant, we first propose a learnable low-pass filter based on a self-attention mechanism to adaptively explore the global contexts for better modeling the low-frequency information. Then we embed it into a Residual Low-Pass Filter (RLPF) module, which involves an additional fully convolutional neural network with the standard residual learning to model the high-frequency information. We formulate the RLPF module into an end-to-end trainable network based on an encoder and decoder architecture and develop a wavelet-based feature fusion to fuse the multi-scale features. Experimental results show that our method performs favorably against state-of-the-art ones on commonly-used benchmarks.