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Sufficient denoising is often an important first step for image processing. In recent decades, deep neural networks (DNNs) have been widely used for image denoising. Most DNN\u2010based image denoising methods require a large\u2010scale dataset or focus on supervised settings, in which single\/pairs of clean images or a set of noisy images are required. This poses a significant burden on the image acquisition process. Moreover, denoisers trained on datasets of limited scale may incur over\u2010fitting. To mitigate these issues, a new self\u2010supervised framework for image denoising based on the Tucker low\u2010rank tensor approximation is introduced. With the proposed design, the authors are able to characterize the denoiser with fewer parameters and train it based on a single image, which considerably improves the model generalizability and reduces the cost of data acquisition. Extensive experiments on both synthetic and real\u2010world noisy images have been conducted. Empirical results show that the proposed method outperforms existing non\u2010learning\u2010based methods (e.g. low\u2010pass filter, non\u2010local mean), single\u2010image unsupervised denoisers (e.g. DIP, NN+BM3D) evaluated on both in\u2010sample and out\u2010sample datasets. The proposed method even achieves comparable performances with some supervised methods (e.g. DnCNN).<\/jats:p>","DOI":"10.1049\/ipr2.13205","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T10:17:24Z","timestamp":1767003444000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing convolutional neural network generalizability via low\u2010rank weight approximation"],"prefix":"10.1049","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3850-5587","authenticated-orcid":false,"given":"Chenyin","family":"Gao","sequence":"first","affiliation":[{"name":"Department of Statistics North Carolina State University Raleigh, North Carolina USA"}]},{"given":"Shu","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Statistics North Carolina State University Raleigh, North Carolina USA"}]},{"given":"Anru R.","family":"Zhang","sequence":"additional","affiliation":[{"name":"Departments of Biostatistics &amp; Bioinformatics Computer Science, Mathematics, and Statistical Science, Duke University Durham, North Carolina USA"}]}],"member":"265","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"issue":"5","key":"e_1_2_10_2_1","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1109\/MSP.2017.2717489","article-title":"Image restoration: From sparse and low\u2010rank priors to deep priors [lecture notes]","volume":"34","author":"Zhang L.","year":"2017","journal-title":"IEEE Signal Process Mag."},{"issue":"2","key":"e_1_2_10_3_1","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1093\/jmicro\/dfp052","article-title":"High\u2010resolution low\u2010dose scanning transmission electron microscopy","volume":"59","author":"Buban J. 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