基于多级光照激励与联合损失约束的低光图像增强网络

doi:10.11896/jsjkx.250400138

Abstract

Abstract: In computer vision applications such as autonomous driving and simultaneous localization and mapping(SLAM),low-light images frequently suffer from diminished contrast,noise interference,and detail loss,significantly impairing visual perception systems.Existing low-light enhancement methods exhibit limitations in noise suppression and color fidelity while demonstrating weak cross-scenario generalization capabilities.To address these challenges,this paper proposes a deep learning-based low-light enhancement approach using an improved Retinexformer architecture.The proposed method achieves effective enhancement through multi-stage feature excitation,global illumination adjustment,and multi-dimensional constrained optimization strategies.Firstly,it constructs MIFIB(Multi-level Illumination Feature Incentive Block) that enhances feature representation through normalization and an advanced channel attention mechanism,strengthening illumination modeling.Secondly,it designs a globally-aware IAB(Illumination Adjustment Block) to optimize illumination distribution in enhanced images.Finally,it introduces a multi-dimensional joint loss optimization strategy incorporating structural similarity constraints,semantic feature constraints,and color intensity consistency constraints to comprehensively guide model learning.Experimental results demonstrate that the proposed method achieves superior performance over state-of-the-art methods on the LOL benchmark in metrics including PSNR and SSIM.Generalization tests on SYNTHIA and Terrasentia datasets further validate the proposed method's advantages in noise suppression,color preservation,and detail retention.Moreover,quantitative evaluation in stereo matching-based 3D reconstruction shows that the proposed method reduces endpoint error(EPE) by 0.5 pixels－a 22.1% improvement compared to non-enhanced methods－confirming the advantages of the proposed method in maintaining scene geometric consistency and improving the robustness of depth perception.

Key words: Low-light image enhancement, Feature squeeze & excitation, Illumination adjustment, Deep learning, Stereo matching

CLC Number:

TP391.41

JIAO Hanbing, KANG Junhua, XIAO Teng, DENG Fei. Low-light Image Enhancement Network Based on Multi-level Illumination Excitation and JointLoss Constraint[J].Computer Science, 2026, 53(7): 62-70.

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Low-light Image Enhancement Network Based on Multi-level Illumination Excitation and JointLoss Constraint

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