针对图像曝光不均问题，提出自适应调制与动态优化新方法。
Rethinking Exposure Correction for Spatially Non-uniform Degradation
- 设计空间信号编码器生成局部自适应调制权重
- 引入基于HSL的补偿模块提升色彩保真度
- 采用不确定性驱动的非均匀损失函数优化局部修复
真实世界中的曝光校正面临空间非均匀退化挑战，同一图像中常存在多种曝光误差。现有方法多基于均匀假设，依赖全局聚合调制信号，仅捕捉整体曝光趋势；优化时也使用统一全局尺度的重建损失，忽视区域间差异化的校正需求。为此，本文提出专为非均匀性设计的新范式：引入空间信号编码器预测空间自适应调制权重，用于引导多个查表实现图像变换，并结合基于HSL的补偿模块提升色彩保真度。在优化层面，提出一种基于不确定性的非均匀损失，根据局部恢复不确定性动态分配优化焦点，更契合真实曝光误差的异质特性。大量实验表明，该方法在定性和定量上均优于当前最先进方法。代码已开源。
原文摘要 · Abstract (English)
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.
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