BANet: A Blur-aware Attention Network for Dynamic Scene Deblurring
Fu-Jen Tsai Yan-Tsung Peng Chung-Chi TsaiYen-Yu Lin, Chia-Wen Lin, ,*equal contribution Thanks: Manuscript received August 16, 2021; revised May 17, 2022 and August 30, 2022; accepted October 13, 2022. Date of publication month day, 2022; date of current version month day, 2022. This work was funded in part by National Science and Technology Council (NSTC) under grants 110-2634-F-002-050, 111-2628-E-A49-025-MY3, 111-2221-E-004-010, and in part by Qualcomm Technologies, Inc., through a Taiwan University Research Collaboration Project, under Grant NAT-487844. The computational and storage resources for this project were provided in part by National Center for High-performance Computing (NCHC) of National Applied Research Laboratories (NARLabs), Hsinchu, Taiwan. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Wangmeng Zuo. (Corresponding author: Chia-Wen Lin) Thanks: F.-J. Tsai is with the Department of Electrical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan. E-mail: Thanks: Y.-T. Peng is with the Department of Computer Science, National Chengchi University, Taipei 116011, Taiwan. E-mail: Thanks: C.-C. Tsai is with Qualcomm Technologies, Inc., San Diego, CA 92121, USA. E-mail: Thanks: Y.-Y. Lin is with the Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 300093, Taiwan. E-mail: Thanks: C.-W. Lin is with the Department of Electrical Engineering, National Tsing Hua University, Hsinchu 300044, Taiwan, and with the Electronic and Optoelectronic System Research Laboratories, Industrial Technology Research Institute, Hsinchu 310401, Taiwan. (e-mail:
Abstract
Image motion blur results from a combination of object motions and camera shakes, and such blurring effect is generally directional and non-uniform. Previous research attempted to solve non-uniform blurs using self-recurrent multi-scale, multi-patch, or multi-temporal architectures with self-attention to obtain decent results. However, using self-recurrent frameworks typically leads to a longer inference time, while inter-pixel or inter-channel self-attention may cause excessive memory usage. This paper proposes a Blur-aware Attention Network (BANet), that accomplishes accurate and efficient deblurring via a single forward pass. Our BANet utilizes region-based self-attention with multi-kernel strip pooling to disentangle blur patterns of different magnitudes and orientations and cascaded parallel dilated convolution to aggregate multi-scale content features. Extensive experimental results on the GoPro and RealBlur benchmarks demonstrate that the proposed BANet performs favorably against the state-of-the-arts in blurred image restoration and can provide deblurred results in real-time.
原文 arXiv:2101.07518;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2101.07518v4