基于通道选择性归一化的广义亮度自适应
Generalized Lightness Adaptation with Channel Selective Normalization
AI总结：
针对现有亮度自适应方法泛化能力弱的问题，提出通道选择性归一化（CSNorm）算法，通过交替训练识别亮度相关通道并仅对其归一化，仅单条件训练即可泛化到未知亮度条件，有效提升现有方法性能。
摘要：
亮度自适应对图像处理的成功至关重要，可避免意外的视觉质量下降，其涵盖多个方面，例如低光照图像增强、图像润饰和逆色调映射。现有方法通常在其训练过的亮度条件下表现良好，但由于泛化能力有限，在未知亮度条件下表现不佳。为解决这一局限，我们提出了一种新颖的广义亮度自适应算法，该算法通过通道滤波设计扩展了传统归一化技术，被称为通道选择性归一化（Channel Selective Normalization，CSNorm）。所提出的CSNorm会有针对性地对亮度相关通道的统计量进行归一化，同时保持其他通道不变，以提升特征的泛化性和判别性。为优化CSNorm，我们提出了一种交替训练策略，可有效识别亮度相关通道。配备我们的CSNorm的模型仅需在一种亮度条件下训练，就能很好地泛化到未知的亮度条件。在多个基准数据集上的实验结果证明了CSNorm在提升现有亮度自适应方法的泛化能力方面的有效性。代码可在https://github.com/mdyao/CSNorm获取。
英文摘要：
Lightness adaptation is vital to the success of image processing to avoid unexpected visual deterioration, which covers multiple aspects, e.g., low-light image enhancement, image retouching, and inverse tone mapping. Existing methods typically work well on their trained lightness conditions but perform poorly in unknown ones due to their limited generalization ability. To address this limitation, we propose a novel generalized lightness adaptation algorithm that extends conventional normalization techniques through a channel filtering design, dubbed Channel Selective Normalization (CSNorm). The proposed CSNorm purposely normalizes the statistics of lightness-relevant channels and keeps other channels unchanged, so as to improve feature generalization and discrimination. To optimize CSNorm, we propose an alternating training strategy that effectively identifies lightness-relevant channels. The model equipped with our CSNorm only needs to be trained on one lightness condition and can be well generalized to unknown lightness conditions. Experimental results on multiple benchmark datasets demonstrate the effectiveness of CSNorm in enhancing the generalization ability for the existing lightness adaptation methods. Code is available at https://github.com/mdyao/CSNorm.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
