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论文类型：会议论文

发表时间：2021-04-12

页面范围：2638-2642

关键字：Low-light Image Enhancement; Deep Learning; Physical Principle; Multi-Scale Aggregation

摘要：The under-exposure and low-light environments are common to degrade the image-quality with invisible information. To ameliorate this case, a copious of low-light image enhancement methods are developed. However, these existing works are hard to handle extremely low-light conditions with noises, even well-known network-based methods. To address this issue, we develop a Principle-inspired Multi-scale Aggregation Network (PMA-Net) to simultaneously achieve the exposure enhancement and noises removal. Specifically, we establish a pioneering principle-inspired connection to present the physical principle in the inside of the network, to strengthen the structural depict. Subsequently, we propose a multi-scale aggregation strategy to eliminate the noises in the enhanced results. Sufficient ablation studies manifest the effectiveness of our PMA-Net. Extensive qualitative and quantitative comparisons with other state-of-the-art methods are conducted to fully indicates our outstanding performance.