Generative Strategy for Low and Normal Light Image Pairs with Enhanced Statistical Fidelity

Cheuk Yiu Chan, Wan Chi Siu, Yuk Hee Chan, H. Anthony Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

Low light image enhancement remains challenging due to limited availability of real low/normal light image pairs for training. Simple image simulation techniques used for data augmentation fail to accurately model noise and distortions present in real low light photos. In this work, we propose N2LDiff, a novel generative model leveraging diffusion processes to synthesize realistic low light images from normal light counterparts. Our model leverages the noise modeling capabilities of diffusion processes to generate low light images with accurate noise, blurring, and color distortions. We make the following key contributions: (1) We develop a novel N2LDiff model that can generate varied low light images from the same normal light input via diffusion processes. (2) We introduce a new benchmark for low light image synthesis using existing datasets. (3) Leveraging N2LDiff, we construct a large-scale low light dataset. Our generated data will facilitate training and evaluation of deep learning models for low light enhancement tasks.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Consumer Electronics, ICCE 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350324136
DOIs
Publication statusPublished - Feb 2024
Event2024 IEEE International Conference on Consumer Electronics, ICCE 2024 - Las Vegas, United States
Duration: 6 Jan 20248 Jan 2024

Publication series

NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
ISSN (Print)0747-668X
ISSN (Electronic)2159-1423

Conference

Conference2024 IEEE International Conference on Consumer Electronics, ICCE 2024
Country/TerritoryUnited States
CityLas Vegas
Period6/01/248/01/24

Keywords

  • Data Augmentation
  • Diffusion
  • Generative Model
  • Low Light Image Enhancement

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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