{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T12:35:59Z","timestamp":1755693359565},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T00:00:00Z","timestamp":1641945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,4,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The most prominent challenges in compressive sensing are seeking the domain where an image is represented sparsely and hence be faithfully recovered to obtain high-quality results. This paper introduces an approach for image compression and recovery. The proposed approach involves two phases: the initial step is the compression phase, and the second step is the recovery phase. Initially, the medical image is subjected to the compression module wherein the self-similarity and the 3-dimensional (3D) transform are adapted for compressing the image. Then, in the recovery phase, the compressive sensing recovery is performed based on structural similarity index measure (SSIM)-based collaborative sparsity measure (S-CoSM), and the novel optimization algorithm, named Taylor-based Sunflower optimization (Taylor-SFO) algorithm. An effective S-CoSM measure is designed by modifying the CoSM using the SSIM metric. The proposed Taylor-SFO will be designed by integrating the Taylor series with the sunflower optimization (SFO) algorithm. The performance of the proposed Taylor-SFO approach is evaluated for matrices SSIM of 0.9412 and peak signal to noise ratio of 57.57\u00a0dB.<\/jats:p>","DOI":"10.1093\/comjnl\/bxab202","type":"journal-article","created":{"date-parts":[[2021,12,31]],"date-time":"2021-12-31T12:08:04Z","timestamp":1640952484000},"page":"873-887","source":"Crossref","is-referenced-by-count":2,"title":["Taylor Sun Flower Optimization-Based Compressive Sensing for Image Compression and Recovery"],"prefix":"10.1093","volume":"66","author":[{"given":"Sekar","family":"R","sequence":"first","affiliation":[{"name":"Assistant Professor , , Tiruvallur, India"},{"name":"Sri Venkateshwara Institute of Science and Technology , , Tiruvallur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ravi","family":"G","sequence":"additional","affiliation":[{"name":"Assistant Professor , , Salem, India"},{"name":"Sona College of Technology , , Salem, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,1,12]]},"reference":[{"key":"2023041809380636100_","first-page":"83","article-title":"Review of image compression and encryption techniques","volume":"8","author":"Setyaningsih","year":"2017","journal-title":"Int. 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