Exposure correction aims to enhance images suffering from improper exposure to achieve satisfactory visual effects. Despite recent progress, existing methods generally mitigate either overexposure or underexposure in input images, and they still struggle to handle images with mixed exposure, i.e., one image incorporates both overexposed and underexposed regions. The mixed exposure distribution is non-uniform and leads to varying representation, which makes it challenging to address in a unified process. In this paper, we introduce an effective Region-aware Exposure Correction Network (RECNet) that can handle mixed exposure by adaptively learning and bridging different regional exposure representations. Specifically, to address the challenge posed by mixed exposure disparities, we develop a region-aware de-exposure module that effectively translates regional features of mixed exposure scenarios into an exposure-invariant feature space. Simultaneously, as de-exposure operation inevitably reduces discriminative information, we introduce a mixed-scale restoration unit that integrates exposure-invariant features and unprocessed features to recover local information. To further achieve a uniform exposure distribution in the global image, we propose an exposure contrastive regularization strategy under the constraints of intra-regional exposure consistency and inter-regional exposure continuity. Extensive experiments are conducted on various datasets, and the experimental results demonstrate the superiority and generalization of our proposed method. The code is released at: https://github.com/kravrolens/RECNet.

翻译：曝光校正旨在改善因曝光不当而受损的图像，以实现满意的视觉效果。尽管近期取得了进展，现有方法通常只能缓解输入图像中的过曝或欠曝问题，在处理包含混合曝光的图像（即同一图像中同时存在过曝和欠曝区域）时仍面临困难。混合曝光分布的非均匀性导致表征变化多样，这使得在统一过程中处理该问题颇具挑战。本文提出了一种有效的区域感知曝光校正网络（RECNet），通过自适应学习并桥接不同区域的曝光表征，能够处理混合曝光。具体而言，为应对混合曝光差异带来的挑战，我们设计了一个区域感知去曝光模块，该模块能有效地将混合曝光场景的区域特征转换至曝光不变的特征空间。同时，由于去曝光操作不可避免地会减少判别信息，我们引入了一个混合尺度恢复单元，通过整合曝光不变特征与未处理特征来恢复局部信息。为进一步实现全局图像中均匀的曝光分布，我们提出了一种曝光对比正则化策略，该策略受限于区域内曝光一致性与区域间曝光连续性。我们在多个数据集上进行了广泛实验，实验结果证明了所提方法的优越性与泛化能力。代码已开源：https://github.com/kravrolens/RECNet。