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dc.contributor.author | Panagiotou, Savvas![]() |
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dc.contributor.author | Bosman, Anna![]() |
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dc.date.accessioned | 2024-08-26T07:59:21Z | |
dc.date.available | 2024-08-26T07:59:21Z | |
dc.date.issued | 2024-12 | |
dc.description | DATA AVAILABILITY : The public link to the code is specified in the manuscript. The datasets are listed in the manuscript and are open source/widely available. | en_US |
dc.description.abstract | Low-light image enhancement (LLIE) techniques attempt to increase the visibility of images captured in low-light scenarios. However, as a result of enhancement, a variety of image degradations such as noise and color bias are revealed. Furthermore, each particular LLIE approach may introduce a different form of flaw within its enhanced results. To combat these image degradations, post-processing denoisers have widely been used, which often yield oversmoothed results lacking detail. We propose using a diffusion model as a post-processing approach, and we introduce Low-light Post-processing Diffusion Model (LPDM) in order to model the conditional distribution between under-exposed and normally-exposed images. We apply LPDM in a manner which avoids the computationally expensive generative reverse process of typical diffusion models, and post-process images in one pass through LPDM. Extensive experiments demonstrate that our approach outperforms competing post-processing denoisers by increasing the perceptual quality of enhanced low-light images on a variety of challenging low-light datasets. Source code is available at https://github.com/savvaki/LPDM. | en_US |
dc.description.department | Computer Science | en_US |
dc.description.librarian | hj2024 | en_US |
dc.description.sdg | SDG-09: Industry, innovation and infrastructure | en_US |
dc.description.sponsorship | The National Research Foundation (NRF) of South Africa. | en_US |
dc.description.uri | https://www.elsevier.com/locate/pr | en_US |
dc.identifier.citation | Panagiotou, S. & Bosman, A.S. 2024, 'Denoising diffusion post-processing for low-light image enhancement', Pattern Recognition, vol. 156, art. 110799, pp. 1-13, doi : 10.1016/j.patcog.2024.110799. | en_US |
dc.identifier.issn | 0031-3203 (print) | |
dc.identifier.issn | 1873-5142 (online) | |
dc.identifier.other | 10.1016/j.patcog.2024.110799 | |
dc.identifier.uri | http://hdl.handle.net/2263/97851 | |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC license. | en_US |
dc.subject | Low-light image enhancement (LLIE) | en_US |
dc.subject | Diffusion model | en_US |
dc.subject | Denoising | en_US |
dc.subject | Post-processing | en_US |
dc.subject | SDG-09: Industry, innovation and infrastructure | en_US |
dc.subject | Low-light post-processing diffusion model (LPDM) | en_US |
dc.title | Denoising diffusion post-processing for low-light image enhancement | en_US |
dc.type | Article | en_US |