{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T08:03:18Z","timestamp":1767168198694,"version":"build-2238731810"},"reference-count":25,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,4,16]],"date-time":"2021-04-16T00:00:00Z","timestamp":1618531200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Pattani Campus Research Fund","award":["SAT63030065"],"award-info":[{"award-number":["SAT63030065"]}]},{"DOI":"10.13039\/501100004508","name":"Prince of Songkla University","doi-asserted-by":"publisher","award":["SAT63030065"],"award-info":[{"award-number":["SAT63030065"]}],"id":[{"id":"10.13039\/501100004508","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Computational Intelligence and Soft Computing"],"published-print":{"date-parts":[[2021,4,16]]},"abstract":"<jats:p>This paper introduces an efficient deblurring image method based on a convolution-based and an iterative concept. Our method does not require specific conditions on images, so it can be widely applied for unspecific generic images. The kernel estimation is firstly performed and then will be used to estimate a latent image in each iteration. The final deblurred image is obtained from the convolution of the blurred image with the final estimated kernel. However, image deblurring is an ill-posed problem due to the nonuniqueness of solutions. Therefore, we propose a smoothing function, unlike previous approaches that applied piecewise functions on estimating a latent image. In our approach, we employ L2-regularization on intensity and gradient prior to converging to a solution of the deblurring problem. Moreover, our work is based on the quadratic splitting method. It guarantees that each subproblem has a closed-form solution. Various experiments on synthesized and real-world images confirm that our approach outperforms several existing methods, especially on the images corrupted by noises. Moreover, our method gives more reasonable and more natural deblurred images than those of other methods.<\/jats:p>","DOI":"10.1155\/2021\/6684345","type":"journal-article","created":{"date-parts":[[2021,4,17]],"date-time":"2021-04-17T15:35:12Z","timestamp":1618673712000},"page":"1-10","source":"Crossref","is-referenced-by-count":4,"title":["An Efficient Blind Image Deblurring Using a Smoothing Function"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1833-0863","authenticated-orcid":true,"given":"Kittiya","family":"Khongkraphan","sequence":"first","affiliation":[{"name":"Faculty of Science and Technology, Prince of Songkla University, Pattani 94000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1808-1545","authenticated-orcid":true,"given":"Aniruth","family":"Phonon","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Prince of Songkla University, Pattani 94000, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0713-7301","authenticated-orcid":true,"given":"Sainuddeen","family":"Nuiphom","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, Prince of Songkla University, Pattani 94000, Thailand"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1145\/1141911.1141956"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1145\/1618452.1618491"},{"key":"3","first-page":"157","article-title":"Two-phase kernel estimation for robust motion deblurring","author":"L. 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