{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/129318"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/129318","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Zero-Shot Low Light Image Enhancement with Diffusion Prior","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. 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The submission was exported from vireo on 2025-10-19 without embargo terms","The student, Joshua Cho, accepted the attached license on 2025-05-04 at 14:40.","The student, Joshua Cho, submitted this Thesis for approval on 2025-05-04 at 14:49.","This Thesis was approved for publication on 2025-05-07 at 13:06.","DSpace SAF Submission Ingestion Package generated from Vireo submission #22202 on 2025-10-19 at 18:12:30","In this work, we present a simple yet highly effective \"free lunch\" solution for low-light image enhancement (LLIE), which aims to restore low-light images as if acquired in well-illuminated environments. Our method necessitates no optimization, training, fine-tuning, text conditioning, or hyperparameter adjustments, yet it consistently reconstructs low-light images with superior fidelity. Specifically, we leverage a pre-trained text-to-image diffusion prior, learned from training on a large collection of natural images, and the features present in the model itself to guide the inference, in contrast to existing methods that depend on customized constraints. Comprehensive quantitative evaluations demonstrate that our approach outperforms SOTA methods on established datasets, while qualitative analyses indicate enhanced color accuracy and the rectification of subtle chromatic deviations. Furthermore, additional experiments reveal that our method, without any modifications, achieves SOTA-comparable performance in the auto white balance (AWB) task."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Zero-Shot Low Light Image Enhancement with Diffusion Prior"]}]}],"canonical_facts":{"dc:contributor":["Forsyth, David A."],"dc:creator":["Cho, Joshua"],"dc:date":["2025-05-07","2025-05"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. 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Specifically, we leverage a pre-trained text-to-image diffusion prior, learned from training on a large collection of natural images, and the features present in the model itself to guide the inference, in contrast to existing methods that depend on customized constraints. Comprehensive quantitative evaluations demonstrate that our approach outperforms SOTA methods on established datasets, while qualitative analyses indicate enhanced color accuracy and the rectification of subtle chromatic deviations. Furthermore, additional experiments reveal that our method, without any modifications, achieves SOTA-comparable performance in the auto white balance (AWB) task."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/129318"],"dc:language":["en","eng"],"dc:rights":["Copyright 2025 Joshua Cho"],"dc:subject":["Low Light Image Enhancement","Auto White Balance"],"dc:title":["Zero-Shot Low Light Image Enhancement with Diffusion Prior"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:04Z"}