A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2506.18323v1
• Date: Mon, 23 Jun 2025 06:11:55 GMT
• Title: A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement
• Authors: Muhammad Azeem Aslam, Hassan Khalid, Nisar Ahmed,
• Abstract summary: LucentVisionNet is a novel zero-shot learning framework for low-light image enhancement.<n>Our framework achieves high visual quality, structural consistency, and computational efficiency.<n>It is well-suited for deployment in real-world applications such as mobile photography, surveillance, and autonomous navigation.
• Score: 3.55026004901472
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Low-light image enhancement remains a challenging task, particularly in the absence of paired training data. In this study, we present LucentVisionNet, a novel zero-shot learning framework that addresses the limitations of traditional and deep learning-based enhancement methods. The proposed approach integrates multi-scale spatial attention with a deep curve estimation network, enabling fine-grained enhancement while preserving semantic and perceptual fidelity. To further improve generalization, we adopt a recurrent enhancement strategy and optimize the model using a composite loss function comprising six tailored components, including a novel no-reference image quality loss inspired by human visual perception. Extensive experiments on both paired and unpaired benchmark datasets demonstrate that LucentVisionNet consistently outperforms state-of-the-art supervised, unsupervised, and zero-shot methods across multiple full-reference and no-reference image quality metrics. Our framework achieves high visual quality, structural consistency, and computational efficiency, making it well-suited for deployment in real-world applications such as mobile photography, surveillance, and autonomous navigation.

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