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LL-Refiner: Learning Adaptive Refinement for Ultra-High-Definition Low-Light Image Enhancement
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DOI：10.1109/JAS.2026.125939.png)
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Abstract
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Low-light image enhancement aims to address various degradations in low-light conditions, such as low illumination, noise pollution, color distortion, and missing scene content. With advances in digital imaging technology, the resolution of captured images has seen substantial improvements. This poses new challenges in achieving good enhancement performance for multi-scale details in ultra-high-definition images, as well as in managing the overhead for supporting the use of consumer-grade GPUs. In this paper, we proposed learning the adaptive refinement framework for ultra-high-definition image enhancement, termed LL-Refiner. It integrates the advantages of hierarchical adaptive refinement guided by coarse enhancement results to enhance the ultra-high-definition images. In detail, firstly, we conduct the coarse enhancement on the low resolution image by employing a Transformer-based coarse enhancement network. Secondly, the coarse enhancement output is fed into the adapted refinement injection. It assigns resolution-aware inputs as guidance to the corresponding adaptive aggregation module, which interacts with the backbone features of the adaptive refinement network. Ultimately, the adaptive refinement network incorporates a combination of hierarchical dense residual connection modules and lightweight convolutional modules at different resolution stages. Also, it integrates a multi-scale enhanced perceptual loss to progressively achieve ultra-high-definition image enhancement. Extensive experiments on ultra-high-definition image enhancement validate the effectiveness and superiority of the proposed method. Our code is publicly available at https://github.com/XunpengYi/LL-Refiner.
Keywords:
Adaptive refinement
low-light image enhancement
ultra-high-definition image
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