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Syst."],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Blind face deblurring aims to recover a sharper face from its unknown degraded version (i.e., different motion blur, noise). However, most previous works typically rely on degradation facial priors extracted from low-quality inputs, which generally leads to unlifelike deblurring results. In this paper, we propose a multi-scale progressive face-deblurring generative adversarial network (MPFD-GAN) that requires no facial priors to generate more realistic multi-scale deblurring results by one feed-forward process. Specifically, MPFD-GAN mainly includes two core modules: the feature retention module and the texture reconstruction module (TRM). The former can capture non-local similar features by full advantage of the different receptive fields, which facilitates the network to recover the complete structure. The latter adopts a supervisory attention mechanism that fully utilizes the recovered low-scale face to refine incoming features at every scale before propagating them further. Moreover, TRM extracts the high-frequency texture information from the recovered low-scale face by the Laplace operator, which guides subsequent steps to progressively recover faithful face texture details. Experimental results on the CelebA, UTKFace and CelebA-HQ datasets demonstrate the effectiveness of the proposed network, which achieves better accuracy and visual quality against state-of-the-art methods.<\/jats:p>","DOI":"10.1007\/s40747-022-00865-9","type":"journal-article","created":{"date-parts":[[2022,9,13]],"date-time":"2022-09-13T09:03:28Z","timestamp":1663059808000},"page":"1439-1453","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multi-scale progressive blind face deblurring"],"prefix":"10.1007","volume":"9","author":[{"given":"Hao","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Canghong","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Xian","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Linfeng","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3341-4034","authenticated-orcid":false,"given":"Xiaojie","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Xi","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Jiancheng","family":"Lv","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,13]]},"reference":[{"issue":"8","key":"865_CR1","doi-asserted-by":"publisher","first-page":"3502","DOI":"10.1109\/TIP.2012.2192126","volume":"21","author":"G Boracchi","year":"2012","unstructured":"Boracchi G, Foi A (2012) Modeling the performance of image restoration from motion blur. 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