Rethinking Exposure Correction for Spatially Non-uniform Degradation

• URL: http://arxiv.org/abs/2604.04136v1
• Date: Sun, 05 Apr 2026 14:41:59 GMT
• Title: Rethinking Exposure Correction for Spatially Non-uniform Degradation
• Abstract summary: We propose a new exposure correction paradigm designed for spatial non-uniformity.<n>Specifically, we introduce a Spatial Signal to predict spatially adaptive modulation weights.<n>Our method achieves superior qualitative and quantitative performance compared with state-of-the-art methods.
• Score: 41.78840354779858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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