EnlightenNet: Illuminating Low Light Images Through Multi-Channel Deep Learning with Logarithmic Feature Fusion

Le résumé fourni par la source

Low-light images have less contrast and blurry details, which make them difficult for humans to see and difficult for computer vision algorithms to process. Traditional restoration techniques often fall short in retaining fine details and delivering visually appealing outcomes due to their reliance on global adjustments and manually designed features. While recent advancements in deep learning have shown promise in this area, they still struggle to effectively manage the wide range of illumination conditions encountered in low-light scenarios. To address these challenges, we propose “EnlightenNet, ‘’ a novel multi-channel deep learning framework specifically designed for low-light image restoration. The framework utilizes a dual-channel strategy: the first channel directly extracts intrinsic features from the input image, whereas the second channel captures features from a negatively logarithmic transformed feature space. Both channels operate within a unified Convolutional Neural Network (CNN) structure, which includes a shallow Local Feature Encoder (LFE) followed by a purposefully designed Multi-Channel Feature Dependency Encoder (MCFDE). The outputs from both channels are combined, and another Local Feature Encoder (LFE) is applied to reconstruct the enhanced image. By integrating logarithmic transformation in one channel and direct feature extraction in the other, the model can capture subtle details under low-light conditions. Significant experimental comparisons with cutting-edge techniques demonstrate the efficacy of the proposed model.

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