Low Light Image Enhancement via Global and Local Context Modeling

TL;DR

This paper proposes a novel deep neural network that models both global and local contexts to significantly improve low-light image enhancement, outperforming existing methods on multiple datasets.

Contribution

It introduces a global context module and a dense residual block to better exploit spatial dependencies at multiple scales for low-light image enhancement.

Findings

Achieves higher PSNR on MIT-Adobe FiveK dataset (24.45 dB) compared to previous methods.

Performs favorably on LoL and SID datasets in terms of image fidelity metrics.

Effectively models spatial correlations and local details for enhanced image quality.

Abstract

Images captured under low-light conditions manifest poor visibility, lack contrast and color vividness. Compared to conventional approaches, deep convolutional neural networks (CNNs) perform well in enhancing images. However, being solely reliant on confined fixed primitives to model dependencies, existing data-driven deep models do not exploit the contexts at various spatial scales to address low-light image enhancement. These contexts can be crucial towards inferring several image enhancement tasks, e.g., local and global contrast, brightness and color corrections; which requires cues from both local and global spatial extent. To this end, we introduce a context-aware deep network for low-light image enhancement. First, it features a global context module that models spatial correlations to find complementary cues over full spatial domain. Second, it introduces a dense residual block that captures local context with a relatively large receptive field. We evaluate the proposed approach using three challenging datasets: MIT-Adobe FiveK, LoL, and SID. On all these datasets, our method performs favorably against the state-of-the-arts in terms of standard image fidelity metrics. In particular, compared to the best performing method on the MIT-Adobe FiveK dataset, our algorithm improves PSNR from 23.04 dB to 24.45 dB.