Real-world exposure correction is fundamentally challenged by spatially non-uniform degradations, where diverse exposure errors frequently coexist within a single image. However, existing exposure correction methods are still largely developed under a predominantly uniform assumption. Architecturally, they typically rely on globally aggregated modulation signals that capture only the overall exposure trend. From the optimization perspective, conventional reconstruction losses are usually derived under a shared global scale, thus overlooking the spatially varying correction demands across regions. To address these limitations, we propose a new exposure correction paradigm explicitly designed for spatial non-uniformity. Specifically, we introduce a Spatial Signal Encoder to predict spatially adaptive modulation weights, which are used to guide multiple look-up tables for image transformation, together with an HSL-based compensation module for improved color fidelity. Beyond the architectural design, we propose an uncertainty-inspired non-uniform loss that dynamically allocates the optimization focus based on local restoration uncertainties, better matching the heterogeneous nature of real-world exposure errors. Extensive experiments demonstrate that our method achieves superior qualitative and quantitative performance compared with state-of-the-art methods. Code is available at https://github.com/FALALAS/rethinkingEC.

翻译：现实世界中的曝光校正面临的根本挑战是空间非均匀降质，即单张图像中常同时存在多种曝光误差。然而，现有曝光校正方法大多仍基于均匀假设构建。在架构上，这些方法通常依赖于全局聚合的调制信号，仅能捕捉整体曝光趋势；从优化角度看，传统重建损失通常基于共享全局尺度推导，忽视了区域间空间变化的校正需求。为解决上述局限，我们提出一种专为空间非均匀性设计的新型曝光校正范式。具体而言，我们引入空间信号编码器以预测空间自适应调制权重，用于指导多个查找表完成图像变换，并辅以基于HSL的补偿模块提升色彩保真度。除架构设计外，我们还提出一种基于不确定性的非均匀损失函数，该函数根据局部重建不确定性动态分配优化重点，更契合真实世界曝光误差的异质特性。大量实验表明，本方法在定性与定量性能上均超越现有最优方法。代码已开源至 https://github.com/FALALAS/rethinkingEC。