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The noise that appears in MRI images is commonly governed by a Rician distribution. The bendlets system is a second-order shearlet transform with bent elements. Thus, the bendlets system is a powerful tool with which to represent images with curve contours, such as the brain MRI images, sparsely. By means of the characteristic of bendlets, an adaptive denoising method for microsection images with Rician noise is proposed. In this method, the curve contour and texture can be identified as low-frequency components, which is not the case with other methods, such as the wavelet, shearlet, and so on. It is well known that the Rician noise belongs to a high-frequency channel, so it can be easily removed without blurring the clarity of the contour. Compared with other algorithms, such as the shearlet transform, block matching 3D, bilateral filtering, and Wiener filtering, the values of Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) obtained by the proposed method are better than those of other methods.<\/jats:p>","DOI":"10.3390\/e24070869","type":"journal-article","created":{"date-parts":[[2022,6,25]],"date-time":"2022-06-25T10:39:13Z","timestamp":1656153553000},"page":"869","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Bendlet Transform Based Adaptive Denoising Method for Microsection Images"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3180-2810","authenticated-orcid":false,"given":"Shuli","family":"Mei","sequence":"first","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7211-0741","authenticated-orcid":false,"given":"Aleksey","family":"Kudreyko","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Informatics, Bashkir State Medical University, Lenina Str. 3, 450008 Ufa, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piercarlo","family":"Cattani","sequence":"additional","affiliation":[{"name":"Department of Computer, Control and Management Engineering, University of Rome \u201cLa Sapienza\u201d, Via Ariosto 25, 00185 Roma, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Denis","family":"Baikov","sequence":"additional","affiliation":[{"name":"Department of Surgery, Transplantology and Radiation Diagnostics, Bashkir State Medical University, Lenina Str. 3, 450008 Ufa, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6545-4589","authenticated-orcid":false,"given":"Francesco","family":"Villecco","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ramm, A.G., and Katsevich, A.I. 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