HVI-CIDNet+: Beyond extreme darkness for low-light image enhancement

Publication Type

Journal Article

Version

acceptedVersion

Publication Date

8-2026

Abstract

Low-Light Image Enhancement (LLIE) aims to recover visually pleasing content and details from degraded low-light images. However, existing RGB-based methods often suffer from color bias and brightness artifacts due to inherent high color sensitivity. Although the HSV color space can decouple brightness and color, it introduces noticeable red and black noise artifacts. To address these challenges, we adopt the Horizontal/Vertical-Intensity (HVI) color space for LLIE, which is defined by the HV color map and learnable intensity. The former enforces small distances for red coordinates to alleviate red noise artifacts, while the latter adaptively compresses low-light regions to suppress black noise artifacts. Based on the HVI space, we further propose HVI-CIDNet+, a Color and Intensity Decoupling Network designed to restore degraded content and mitigate color distortion in extremely dark regions. Specifically, HVI-CIDNet+ extracts abundant contextual and degradation-aware priors from low-light images using pre-trained vision-language models and integrates them through a novel Prior-guided Attention Block (PAB). Within PAB, latent semantic priors facilitate content restoration, while degraded representations guide more reliable color correction through a carefully designed cross-attention fusion mechanism. Furthermore, we introduce a Region Refinement Block that combines convolution for information-rich regions with attention for information-scarce regions, enabling accurate brightness adjustment. Extensive experiments on 10 benchmark datasets demonstrate that HVI-CIDNet+ consistently outperforms existing state-of-the-art methods. The code is available at the https://github.com/shikangbiao/CIDNet_extension

Keywords

HVI color space, low-light image enhancement, degraded representations, latent semantic priors, region refinement

Discipline

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

IEEE Transactions on Circuits and Systems for Video Technology

Volume

36

Issue

8

First Page

12126

Last Page

12139

ISSN

1051-8215

Identifier

10.1109/TCSVT.2026.3710237

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TCSVT.2026.3710237

This document is currently not available here.

Share

COinS