Zero-Shot Low Light Image Enhancement with Diffusion Prior

Cho, Joshua

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https://hdl.handle.net/2142/129318 Copy

Description

Title

Zero-Shot Low Light Image Enhancement with Diffusion Prior

Author(s)

Cho, Joshua

Issue Date

2025-05-07

Director of Research (if dissertation) or Advisor (if thesis)

Forsyth, David A

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

Low Light Image Enhancement, Auto White Balance

Abstract

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.

Graduation Semester

2025-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/129318

Copyright and License Information

Copyright 2025 Joshua Cho

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