AI 中文总结
针对遥感图像去阴影的不适定问题，提出BRIC-Net网络，通过LRP、BAGM、SCMM模块实现光照与色度解耦，在多个数据集上取得优于现有方法的去阴影效果。
AI 中文摘要
遥感图像中的阴影会遮挡地表外观并破坏辐射连续性，降低视觉解译及后续分析的可靠性。遥感图像去阴影是一个不适定逆问题，需恢复空间变化的光照，同时保持非阴影区域的色度和辐射一致性。现有方法通常依赖硬阴影掩码进行补偿，或直接回归RGB强度；硬掩码无法充分建模半影渐变，且对定位误差敏感，易产生残影或光晕伪影；直接RGB回归将光照恢复与色度重建纠缠，易引入色偏。为此，我们提出边界可靠光照-颜色交互网络（BRIC-Net），在不同表示层面解决上述问题：明度可靠性先验（LRP）基于CIELAB统计生成感知可靠性的引导；边界自适应门控混合（BAGM）在不确定过渡区域对浅层RGB与明度特征进行门控插值；空间-通道互调制（SCMM）协调深层空间与通道响应，以实现保外观的光照恢复。BRIC-Net在AeroDS-Syn数据集上实现全图峰值信噪比（PSNR）29.46dB，在SRGTA数据集上为27.96dB；在AISD和AeroDS-Real数据集上获得最低的感知图像质量评估器（PIQE）分数，区域评估及组件消融实验进一步验证其在阴影恢复与非阴影区域保留上的有效性。
英文摘要
Shadows in remote sensing images obscure surface appearance and disrupt radiometric continuity, reducing the reliability of visual interpretation and downstream analysis. Remote sensing image deshadowing is an ill-posed inverse problem that requires spatially varying illumination recovery while preserving chromatic and radiometric consistency in non-shadow regions. Existing methods commonly rely on hard shadow masks for compensation or directly regress RGB intensities. Hard masks may inadequately model gradual penumbra variations and are sensitive to localization errors, often producing residual shadows or halo artifacts; direct RGB regression entangles illumination recovery with chromatic reconstruction and can introduce color casts. To this end, we propose the Boundary-Reliable Illumination-Color Interaction Network (BRIC-Net), which decouples these failures at different representation levels. A Lightness Reliability Prior (LRP) derives reliability-aware guidance from CIELAB statistics. Boundary-Adaptive Gated Mixing (BAGM) performs gated interpolation between shallow RGB and lightness features around uncertain transitions, while Spatial-Channel Mutual Modulation (SCMM) coordinates deeper spatial and channel responses for appearance-preserving illumination recovery. BRIC-Net achieves 29.46~dB full-image peak signal-to-noise ratio (PSNR) on AeroDS-Syn and 27.96~dB on SRGTA. It also obtains the lowest Perception-based Image Quality Evaluator (PIQE) scores on AISD and AeroDS-Real. Region-wise evaluations and component ablations further support its effectiveness in shadow recovery and non-shadow preservation.
