AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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.
The proposed MSHCDI-Net is 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 and achieves improved structural consistency and detail restoration in low-light scenes.
SFH-Net is proposed, an horizontal-vertical-intensity (HVI)-guided luminance-chrominance collaborative enhancement framework that achieves a better trade-off among reconstruction accuracy, structural fidelity, and parameter compactness.
A dual-path color-decoupled network, termed DPCDNet, which progressively decouples luminance and chrominance at three stages: source, interaction, and output, offering a new approach for enhancement techniques that balance brightness and color.
A Hierarchical Adaptive Interaction Modulation Network (HAIMNet) designed for LLIE is proposed, which decouples luminance and chromaticity in the Horizontal/Vertical-Intensity color space, and enhances luminance-texture consistency through an inter-branch attention-modulation block (IAMB).
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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