Rethinking Exposure Correction for Spatially Non-uniform Degradation

2026-04-05

AI Summary

This work addresses the challenge of spatially non-uniform exposure degradation commonly observed in real-world images, which existing methods often fail to correct effectively due to their reliance on global uniformity assumptions. To overcome this limitation, the paper proposes a novel paradigm for non-uniform exposure correction that employs a spatial signal encoder to generate adaptive modulation weights. These weights guide a multi-lookup-table transformation combined with an HSL color compensation module to achieve precise local adjustments. Furthermore, a dynamic loss function based on local uncertainty is introduced to optimize restoration quality. The proposed method transcends the constraints of conventional global correction strategies and demonstrates significant improvements over state-of-the-art approaches in both quantitative metrics and visual fidelity.

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.

Problem

• exposure correction
• spatially non-uniform degradation
• real-world exposure errors
• non-uniform exposure

Innovation

• spatially non-uniform degradation
• adaptive modulation
• uncertainty-inspired loss
• exposure correction
• HSL-based compensation