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http://dx.doi.org/10.5909/JBE.2022.27.3.351

Distortion-guided Module for Image Deblurring  

Kim, Jeonghwan (Department of Artificial Intelligence, Konkuk University)
Kim, Wonjun (Department of Electrical and Electronics Engineering, Konkuk University)
Publication Information
Journal of Broadcast Engineering / v.27, no.3, 2022 , pp. 351-360 More about this Journal
Abstract
Image blurring is a phenomenon that occurs due to factors such as movement of a subject and shaking of a camera. Recently, the research for image deblurring has been actively conducted based on convolution neural networks. In particular, the method of guiding the restoration process via the difference between blur and sharp images has shown the promising performance. This paper proposes a novel method for improving the deblurring performance based on the distortion information. To this end, the transformer-based neural network module is designed to guide the restoration process. The proposed method efficiently reflects the distorted region, which is predicted through the global inference during the deblurring process. We demonstrate the efficiency and robustness of the proposed module based on experimental results with various deblurring architectures and benchmark datasets.
Keywords
Image Deblurring; Distortion-guided Module; Transformer; Convolution Neural Network;
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