CodedBGT: Code Bank-Guided Transformer for Low-light Image Enhancement

Dongjie YE, Baoliang CHEN, Shiqi WANG, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Low-light images commonly exhibit issues such as reduced contrast, heightened noise, faded colors, and the absence of critical details. Enhancing these images is challenging due to the complex interplay of various factors. Existing methods primarily focus on learning the intricate mapping between low-light input and normal-light output through well-designed deep neural networks, potentially overlooking the valuable priors inherent in normal-light images. In this paper, we introduce a Code Bank-Guided Transformer (CodedBGT) for low-light image enhancement. Initially, we pre-train a VQGAN on an extensive collection of high-quality normal-light images to capture a high-quality prior. This prior is stored in a discrete codebook along with its corresponding decoded feature space, forming the code bank that guides the enhancement process. To effectively align low-light features with undistorted normal-light code bank features, we design a Code Bank-Guided Block (CBGB) within our enhancement network. The CBGB is integrated into the transformer to aggregate prior information into the enhancement network. Benefiting from the high-quality code bank, our method produces results with more satisfying visual quality. In comparison with the state-of-the-art methods, higher quantitative and qualitative experimental results on the paired dataset and unpaired datasets with various evaluation metrics show the superiority of our method.
Original languageEnglish
Number of pages13
JournalIEEE Transactions on Multimedia
DOIs
Publication statusE-pub ahead of print - 13 May 2024

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