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Appl."],"published-print":{"date-parts":[[2021,2,28]]},"abstract":"<jats:p>\n            In this article, we address the degraded image super-resolution problem in a multi-task learning (MTL) manner. To better share representations between multiple tasks, we propose an all-in-one collaboration framework (ACF) with a learnable \u201cjunction\u201d unit to handle two major problems that exist in MTL\u2014\u201cHow to share\u201d and \u201cHow much to share.\u201d Specifically, ACF consists of a\n            <jats:italic>sharing<\/jats:italic>\n            phase and a\n            <jats:italic>reconstruction<\/jats:italic>\n            phase. Considering the intrinsic characteristic of multiple image degradations, we propose to first deal with the compression artifact, motion blur, and spatial structure information of the input image in parallel under a three-branch architecture in the\n            <jats:italic>sharing<\/jats:italic>\n            phase. Subsequently, in the\n            <jats:italic>reconstruction<\/jats:italic>\n            phase, we up-sample the previous features for high-resolution image reconstruction with a channel-wise and spatial attention mechanism. To coordinate two phases, we introduce a learnable \u201cjunction\u201d unit with a dual-voting mechanism to selectively filter or preserve shared feature representations that come from\n            <jats:italic>sharing<\/jats:italic>\n            phase, learning an optimal combination for the following\n            <jats:italic>reconstruction<\/jats:italic>\n            phase. Finally, a curriculum learning-based training scheme is further proposed to improve the convergence of the whole framework. Extensive experimental results on synthetic and real-world low-resolution images show that the proposed all-in-one collaboration framework not only produces favorable high-resolution results while removing serious degradation, but also has high computational efficiency, outperforming state-of-the-art methods. We also have applied ACF to some image-quality sensitive practical task, such as pose estimation, to improve estimation accuracy of low-resolution images.\n          <\/jats:p>","DOI":"10.1145\/3417333","type":"journal-article","created":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T12:42:08Z","timestamp":1618576928000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Multi-task Learning-based All-in-one Collaboration Framework for Degraded Image Super-resolution"],"prefix":"10.1145","volume":"17","author":[{"given":"Xin","family":"Jin","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Jianfeng","family":"Xu","sequence":"additional","affiliation":[{"name":"Media Recognition Laboratory, KDDI Research, Inc, Fujimino-shi, Japan"}]},{"given":"Kazuyuki","family":"Tasaka","sequence":"additional","affiliation":[{"name":"Media Recognition Laboratory, KDDI Research, Inc, Fujimino-shi, Japan"}]},{"given":"Zhibo","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2021,4,16]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2015.2477680"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.161"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5244\/C.26.135"},{"key":"e_1_2_1_4_1","volume-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555","author":"Chung Junyoung","year":"2014","unstructured":"Junyoung Chung , Caglar Gulcehre , KyungHyun Cho , and Yoshua Bengio . 2014. 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