TL;DR
Results indicate that the proposed luminance–chroma collaborative design effectively improves reconstruction fidelity and structural preservation under the evaluated low-light conditions.
Results validate the effectiveness and robustness of the proposed illumination-aware modeling strategy for low-light image enhancement, IA2former, which effectively captures long-range dependencies, improves detail restoration, and preserves spatial structures under challenging illumination conditions.
Low-light image enhancement aims to improve visual visibility and perceptual quality under challenging illumination conditions. However, conventional convolutional neural networks (CNNs) are inherently limited in modeling long-range dependencies due to their restricted receptive fields, which often leads to insufficient global context modeling and suboptimal restoration results. To address this limitation, we propose MSHCDI-Net, a Multi-Scale Hybrid Cross-Domain Interaction Network that effectively integrates CNN and Transformer branches to jointly capture local texture details and global contextual relationships. Specifically, the proposed framework adopts a hierarchical encoder–decoder architecture to perform multi-scale feature extraction and progressive reconstruction. A cross-domain interaction mechanism is introduced to facilitate effective information exchange between convolutional and Transformer representations across multiple resolutions, enabling complementary modeling of fine-grained structures and long-range dependencies. Through adaptive feature fusion and multi-scale guidance, the network achieves improved structural consistency and detail restoration in low-light scenes. Extensive experiments on several public benchmarks demonstrate the effectiveness of the proposed method. MSHCDI-Net achieves 23.45 dB PSNR / 0.848 SSIM on LOL-v1, 23.74 dB / 0.910 SSIM on LOL-v2-synthetic, and 22.24 dB / 0.868 SSIM on LOL-v2-real, demonstrating competitive performance in both quantitative metrics and visual quality.
Low-light image enhancement remains challenging because brightness amplification often introduces color bias, chromatic noise, and loss of fine details. To address these issues, we propose SFH-Net, an horizontal-vertical-intensity (HVI)-guided luminance-chrominance collaborative enhancement framework. The proposed method operates in the HVI color space, where the H/V chrominance channels and the intensity channel are processed through dedicated branches. In the luminance branch, a frequency-enhanced residual block with a fixed center-square frequency partition provides spectral auxiliary cues for illumination and texture restoration, followed by spatial residual refinement. In the chrominance branch, a U-Net-based chrominance denoiser module predicts signed residual corrections for the H/V channels, suppressing chromatic noise while preserving hue-direction information. During training, a two-stage strategy first stabilizes reconstruction and then introduces adversarial chrominance refinement. Experiments demonstrate that SFH-Net achieves a better trade-off among reconstruction accuracy, structural fidelity, and parameter compactness. The source code is available at: https://github.com/Zhanghuijie-one/SFH-Net.
The fundamental bottleneck in low-light image enhancement is the persistent coupling between luminance and chrominance throughout the pipeline, from input representation and feature interactions to the final output. This coupling prevents existing methods from simultaneously achieving brightness enhancement and color fidelity. To address this problem, we propose a dual-path color-decoupled network, termed DPCDNet, which progressively decouples luminance and chrominance at three stages: source, interaction, and output. Specifically, the Gated Color Fusion (GCF) module fuses complementary luminance cues from HVI and YUV spaces to achieve initial decoupling; the Complementary Mamba Interaction Module (CMIM) combines Mamba-based global modeling with frequency-aware feature enhancement to facilitate complementary interaction between luminance and chrominance features while mitigating feature re-coupling. The Illumination-Aware Attention Module (IAAM) uses illumination-branch features to guide spatially adaptive refinement, thereby completing decoupling at the output stage. On datasets such as LOLv1, LOLv2, MIT-Adobe FiveK, and DICM, DPCDNet achieves significantly better PSNR and SSIM than existing methods. Our method helps to mitigate the full-pipeline luminance-chrominance coupling problem, offering a new approach for enhancement techniques that balance brightness and color.
Low-light image enhancement (LLIE) is essential for enabling reliable nighttime visual perception and improving the performance of downstream vision tasks, including object detection and image segmentation. Under complex illumination conditions, low-light images often suffer from insufficient luminance, loss of structural details, and unstable color reproduction. Existing methods struggle to simultaneously restore luminance, texture, and color in a coherent manner. This paper proposes a Hierarchical Adaptive Interaction Modulation Network (HAIMNet) designed for LLIE. The proposed method decouples luminance and chromaticity in the Horizontal/Vertical-Intensity (HVI) color space, and enhances luminance-texture consistency through an inter-branch attention-modulation block (IAMB). Furthermore, a cross-branch gated affine fusion module (CGAF) is introduced to calibrate features between luminance and chromatic-structural representations, reduce color deviations, and enhance perceptual consistency. Extensive experiments on 11 public datasets demonstrate the effectiveness, robustness, and generalization capability of HAIMNet. The enhanced results exhibit high naturalness and stability under extremely dark and complex illumination conditions. Our code is available at: https://github.com/ZekeWang13/HAIMNet
Experimental results show that the proposed approach achieves better contrast enhancement, noise suppression, and color preservation compared with other methods, and the lightweight architecture and low computational complexity make the method suitable for practical low-light image enhancement applications.
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