@techreport{oai:ipsj.ixsq.nii.ac.jp:00203413,
 author = {Jianfeng, Xu and Kazuyuki, Tasaka and Jianfeng, Xu and Kazuyuki, Tasaka},
 issue = {11},
 month = {Feb},
 note = {In this paper, we address the degraded image super-resolution problem in a multi-task learning (MTL) manner. An all-in-one collaboration framework (ACF) with a learnable junction unit has been proposed to handle two major problems that exist in MTL: How to share and How much to share. Specifically, considering the intrinsic characteristic of multiple image degradations, we propose to first deal with the compression artifact, motion blur (dynamic blur), and spatial structure information of the input image in parallel under a three-branch architecture. Then, based on the feedback from the final super-resolution reconstruction, we train a learnable junction unit with a dual-voting mechanism to selectively filter or preserve these shared feature representations. This unit facilitates collaboration between multi-tasks and learns an optimal combination of different features. Experimental results show that the proposed all-in-one collaboration framework not only produce favorable high-resolution results while removing serious degradation, but also has high computational efficiency, outperforming state-of-the-art methods., In this paper, we address the degraded image super-resolution problem in a multi-task learning (MTL) manner. An all-in-one collaboration framework (ACF) with a learnable junction unit has been proposed to handle two major problems that exist in MTL: How to share and How much to share. Specifically, considering the intrinsic characteristic of multiple image degradations, we propose to first deal with the compression artifact, motion blur (dynamic blur), and spatial structure information of the input image in parallel under a three-branch architecture. Then, based on the feedback from the final super-resolution reconstruction, we train a learnable junction unit with a dual-voting mechanism to selectively filter or preserve these shared feature representations. This unit facilitates collaboration between multi-tasks and learns an optimal combination of different features. Experimental results show that the proposed all-in-one collaboration framework not only produce favorable high-resolution results while removing serious degradation, but also has high computational efficiency, outperforming state-of-the-art methods.},
 title = {A Study on Degraded Image Super-Resolution with Joint De-artifact and Deblurring},
 year = {2020}
}