{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T14:15:56Z","timestamp":1780323356269,"version":"3.54.1"},"reference-count":44,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,14]],"date-time":"2022-12-14T00:00:00Z","timestamp":1670976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The prevalent convolutional neural network (CNN)-based image denoising methods extract features of images to restore the clean ground truth, achieving high denoising accuracy. However, these methods may ignore the underlying distribution of clean images, inducing distortions or artifacts in denoising results. This paper proposes a new perspective to treat image denoising as a distribution learning and disentangling task. Since the noisy image distribution can be viewed as a joint distribution of clean images and noise, the denoised images can be obtained via manipulating the latent representations to the clean counterpart. This paper also provides a distribution-learning-based denoising framework. Following this framework, we present an invertible denoising network, FDN, without any assumptions on either clean or noise distributions, as well as a distribution disentanglement method. FDN learns the distribution of noisy images, which is different from the previous CNN-based discriminative mapping. Experimental results demonstrate FDN\u2019s capacity to remove synthetic additive white Gaussian noise (AWGN) on both category-specific and remote sensing images. Furthermore, the performance of FDN surpasses that of previously published methods in real image denoising with fewer parameters and faster speed.<\/jats:p>","DOI":"10.3390\/s22249844","type":"journal-article","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T03:43:49Z","timestamp":1671075829000},"page":"9844","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network"],"prefix":"10.3390","volume":"22","author":[{"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"The Research School of Computer Science, The Australian National University, Canberra, ACT 2600, Australia"},{"name":"Imaging and Computer Vision, Data61, CSIRO, Canberra, ACT 2600, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeed","family":"Anwar","sequence":"additional","affiliation":[{"name":"The Research School of Computer Science, The Australian National University, Canberra, ACT 2600, Australia"},{"name":"Imaging and Computer Vision, Data61, CSIRO, Canberra, ACT 2600, Australia"},{"name":"The School of Computer Science, The University of Technology Sydney, 15 Broadway Ultimo, Sydney, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenyue","family":"Qin","sequence":"additional","affiliation":[{"name":"The Research School of Computer Science, The Australian National University, Canberra, ACT 2600, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pan","family":"Ji","sequence":"additional","affiliation":[{"name":"The OPPO US Research, San Francisco, CA 94303, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sabrina","family":"Caldwell","sequence":"additional","affiliation":[{"name":"The Research School of Computer Science, The Australian National University, Canberra, ACT 2600, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8356-4909","authenticated-orcid":false,"given":"Tom","family":"Gedeon","sequence":"additional","affiliation":[{"name":"The Research School of Computer Science, The Australian National University, Canberra, ACT 2600, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,14]]},"reference":[{"key":"ref_1","unstructured":"Anwar, S., and Barnes, N. 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