Learning Deformable and Attentive Network for image restoration

Abstract

Image restoration (IR) aims to recover image quality from various degradations. Existing convolutional neural networks (CNN) based IR methods try to improve performance by enlarging the model receptive field with the sacrifice of fine spatial details and extra artifacts. This paper proposes a Deformable and Attentive Network (DANet) to address these problems. In DANet, we propose two novel blocks: Attentive DEformable-convolution Block (ADEB) and Attentive Recurrent Offset Block (AROB). In ADEB, deformable convolution is collaborated with various attention modules to generate more adaptive receptive fields. AROB transfers more attentive texture information among different scales during the encoding/decoding process for ADEB. To further refine DANet, we propose a knowledge distillation scheme to train a light-weighted DANet (DANet-S) with limited performance loss. Extensive experiments on several image benchmark datasets demonstrate that our method achieves state-of-the-art (SOTA) results for various IR tasks, including image denoising, JPEG artifacts removal, and real-world super resolution.