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In practice, color images often suffer from degradations caused by sensor noise, optical blur, compression artifacts, and data loss during the acquisition, transmission, or storage. Unlike grayscale images, color images exhibit high correlations among their RGB channels. Directly extending grayscale restoration methods to color images often leads to issues such as color distortion and structural artifacts. To address these challenges, this paper proposes a novel quaternion\u2010based color image restoration framework. The method integrates low\u2010rank pseudo\u2010norm constraints with saturation\u2010value total variation (SVTV) regularization, effectively enhancing restoration quality in tasks including denoising, deblurring, and inpainting of degraded color images. The proposed algorithm is efficiently solved using the alternating direction method of multipliers (ADMM), and restoration performance is rigorously evaluated through quantitative metrics including peak signal\u2010to\u2010noise ratio (PSNR), structural similarity index measure (SSIM), and S\u2010CIELAB error. Extensive experimental results demonstrate the superior performance of our method compared to existing\u00a0approaches.<\/jats:p>","DOI":"10.1049\/ipr2.70219","type":"journal-article","created":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T09:17:37Z","timestamp":1758964657000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Quaternion\u2010Based Image Restoration via Saturation\u2010Value Total Variation and Pseudo\u2010Norm Regularization"],"prefix":"10.1049","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2131-7368","authenticated-orcid":false,"given":"Zipeng","family":"Fu","sequence":"first","affiliation":[{"name":"School of Electronic and Optical Engineering and the Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense Nanjing University of Science and Technology Nanjing 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