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Syst."],"published-print":{"date-parts":[[2022,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Blur detection is aimed to differentiate the blurry and sharp regions from a given image. This task has attracted much attention in recent years due to its importance in computer vision with the integration of image processing and artificial intelligence. However, blur detection still suffers from problems such as the oversensitivity to image noise and the difficulty in cost\u2013benefit balance. To deal with these issues, we propose an accurate and efficient blur detection method, which is concise in architecture and robust against noise. First, we develop a sequency spectrum-based blur metric to estimate the blurriness of each pixel by integrating a re-blur scheme and the Walsh transform. Meanwhile, to eliminate the noise interference, we propose an adaptive sequency spectrum truncation strategy by which we can obtain an accurate blur map even in noise-polluted cases. Finally, a multi-scale fusion segmentation framework is designed to extract the blur region based on the clustering-guided region growth. Experimental results on benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance and the best balance between cost and benefit. It offers an average <jats:italic>F<\/jats:italic><jats:sub>1<\/jats:sub> score of 0.887, MAE of 0.101, detecting time of 0.7\u00a0s, and training time of 0.5\u00a0s. Especially for noise-polluted blurry images, the proposed method achieves the <jats:italic>F<\/jats:italic><jats:sub>1<\/jats:sub> score of 0.887 and MAE of 0.101, which significantly surpasses other competitive approaches. Our method yields a cost\u2013benefit advantage and noise immunity that has great application prospect in complex sensing environment.<\/jats:p>","DOI":"10.1007\/s40747-021-00592-7","type":"journal-article","created":{"date-parts":[[2021,12,9]],"date-time":"2021-12-09T12:06:30Z","timestamp":1639051590000},"page":"1323-1337","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Noise-immune image blur detection via sequency spectrum truncation"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3326-3723","authenticated-orcid":false,"given":"Xiao","family":"Liang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5780-6553","authenticated-orcid":false,"given":"Xuewei","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Litong","family":"Lyu","sequence":"additional","affiliation":[]},{"given":"Yanjun","family":"Han","sequence":"additional","affiliation":[]},{"given":"Jinjin","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Wenwu","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Jingbo","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,9]]},"reference":[{"key":"592_CR1","doi-asserted-by":"crossref","unstructured":"Yang D, Qin S (2015) Restoration of degraded image with partial blurred regions based on blur detection and classification. 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