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The weighted least squares optimisation strategy that particularly is well-suited for progressively coarsening the original images and simultaneously extract multiscale information has been executed. Subsequently, a SR model by training CNNs based on wavelet analysis has been designed by carrying out wavelet decomposition of optimized images for multiscale representations. Then multiple CNNs have been trained separately to approximate the wavelet multiscale representations. The trained multiple convolutional neural networks characterize medical images in many directions and multiscale frequency bands, and thus facilitate image restoration subject to increased number of variations  depicted in different dimensions and orientations. Finally, the trained CNNs regress wavelet multiscale representations from a LR medical images,  followed by wavelet synthesis that forms a reconstructed HR medical image. The experimental performance indicates that the proposed model SR restoration approach achieve superior SR efficiency over existing comparative methods<\/jats:p>","DOI":"10.1007\/s40747-021-00465-z","type":"journal-article","created":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T05:03:07Z","timestamp":1626843787000},"page":"3089-3104","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["A weighted least squares optimisation strategy for medical image super resolution via multiscale convolutional neural networks for healthcare applications"],"prefix":"10.1007","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0111-9612","authenticated-orcid":false,"given":"Bhawna","family":"Goyal","sequence":"first","affiliation":[]},{"given":"Dawa Chyophel","family":"Lepcha","sequence":"additional","affiliation":[]},{"given":"Ayush","family":"Dogra","sequence":"additional","affiliation":[]},{"given":"Shui-Hua","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,7,20]]},"reference":[{"key":"465_CR1","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.sysarc.2015.11.005","volume":"64","author":"W Wu","year":"2016","unstructured":"Wu W, Yang X, Liu K, Liu Y, Yan B (2016) A new framework for remote sensing image super-resolution: sparse representation-based method by processing dictionaries with multi-type features. 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