Learning Adaptive Lighting via Channel-Aware Guidance

• URL: http://arxiv.org/abs/2412.01493v1
• Date: Mon, 02 Dec 2024 13:44:53 GMT
• Title: Learning Adaptive Lighting via Channel-Aware Guidance
• Authors: Qirui Yang, Peng-Tao Jiang, Hao Zhang, Jinwei Chen, Bo Li, Huanjing Yue, Jingyu Yang,
• Abstract summary: Learning Adaptive Lighting Network (LALNet) is a unified framework capable of processing different light-related tasks. We introduce color-separated features that emphasize the light difference of different color channels and combine them with traditional color-mixed features. Experiments on four representative light-related tasks demonstrate that LALNet significantly outperforms state-of-the-art methods on benchmark tests.
• Score: 22.594897891133556
• License:
• Abstract: Learning lighting adaption is a key step in obtaining a good visual perception and supporting downstream vision tasks. There are multiple light-related tasks (e.g., image retouching and exposure correction) and previous studies have mainly investigated these tasks individually. However, we observe that the light-related tasks share fundamental properties: i) different color channels have different light properties, and ii) the channel differences reflected in the time and frequency domains are different. Based on the common light property guidance, we propose a Learning Adaptive Lighting Network (LALNet), a unified framework capable of processing different light-related tasks. Specifically, we introduce the color-separated features that emphasize the light difference of different color channels and combine them with the traditional color-mixed features by Light Guided Attention (LGA). The LGA utilizes color-separated features to guide color-mixed features focusing on channel differences and ensuring visual consistency across channels. We introduce dual domain channel modulation to generate color-separated features and a wavelet followed by a vision state space module to generate color-mixed features. Extensive experiments on four representative light-related tasks demonstrate that LALNet significantly outperforms state-of-the-art methods on benchmark tests and requires fewer computational resources. We provide an anonymous online demo at https://xxxxxx2025.github.io/LALNet/.

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