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

TL;DR

LucentVisionNet is a zero-shot learning framework that enhances low-light images by integrating multi-scale spatial attention with a deep curve network, achieving superior quality without paired training data.

Contribution

It introduces a novel zero-shot approach combining multi-scale spatial attention and a deep curve network for effective low-light image enhancement.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Achieves high visual quality and structural consistency

Demonstrates computational efficiency suitable for real-world applications

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.