SCRNet: a Retinex Structure-based Low-light Enhancement Model Guided by Spatial Consistency

TL;DR

SCRNet is a novel low-light image enhancement model that uses a Retinex-based structure guided by spatial consistency principles, significantly improving image quality for better computer vision performance.

Contribution

The paper introduces SCRNet, a new Retinex-based model incorporating multi-level spatial consistency to adaptively enhance low-light images.

Findings

Outperforms existing state-of-the-art methods on multiple datasets.

Enhances contrast, reduces noise, and preserves details effectively.

Improves downstream computer vision task performance.

Abstract

Images captured under low-light conditions are often plagued by several challenges, including diminished contrast, increased noise, loss of fine details, and unnatural color reproduction. These factors can significantly hinder the performance of computer vision tasks such as object detection and image segmentation. As a result, improving the quality of low-light images is of paramount importance for practical applications in the computer vision domain.To effectively address these challenges, we present a novel low-light image enhancement model, termed Spatial Consistency Retinex Network (SCRNet), which leverages the Retinex-based structure and is guided by the principle of spatial consistency.Specifically, our proposed model incorporates three levels of consistency: channel level, semantic level, and texture level, inspired by the principle of spatial consistency.These levels of consistency enable our model to adaptively enhance image features, ensuring more accurate and visually pleasing results.Extensive experimental evaluations on various low-light image datasets demonstrate that our proposed SCRNet outshines existing state-of-the-art methods, highlighting the potential of SCRNet as an effective solution for enhancing low-light images.