Decoupling Induction and Multi-Order Attention Drop-Out Gating Based Joint Motion Deblurring and Image Super-Resolution

Yuezhong Chu, Xuefeng Zhang and Heng Liu
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Yuezhong Chu: School of Computer Science and Technology, Anhui University of Technology, Ma’anshan 243002, China
Xuefeng Zhang: School of Computer Science and Technology, Anhui University of Technology, Ma’anshan 243002, China
Heng Liu: School of Computer Science and Technology, Anhui University of Technology, Ma’anshan 243002, China

Mathematics, 2022, vol. 10, issue 11, 1-15

Abstract: Resolution decrease and motion blur are two typical image degradation processes that are usually addressed by deep networks, specifically convolutional neural networks (CNNs). However, since real images are usually obtained through multiple degradations, the vast majority of current CNN methods that employ a single degradation process inevitably need to be improved to account for multiple degradation effects. In this work, motivated by degradation decoupling and multiple-order attention drop-out gating, we propose a joint deep recovery model to efficiently address motion blur and resolution reduction simultaneously. Our degradation decoupling style improves the continence and the efficiency of model construction and training. Moreover, the proposed multi-order attention mechanism comprehensively and hierarchically extracts multiple attention features and fuses them properly by drop-out gating. The proposed approach is evaluated using diverse benchmark datasets including natural and synthetic images. The experimental results show that our proposed method can efficiently complete joint motion blur and image super-resolution (SR).

Keywords: motion deblurring; image super-resolution; multi-order attention; gated learning; decoupling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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