OGG-FR：用于无人机红外图像超分辨率的正交梯度博弈与频率校正
OGG-FR: Orthogonal Gradient Gaming and Frequency Rectification for Unmanned Aerial Vehicle Infrared Image Super-Resolution
AI总结：
针对无人机红外图像超分辨率多损失训练梯度易冲突的问题，提出即插即用的OGG-FR框架，通过分解梯度并结合MGDA等方法优化，在×4、×8尺度降质任务中取得性能提升。
AI中文摘要：
无人机（UAV）红外图像超分辨率旨在恢复微弱的热结构，以部署在资源受限的平台上，因此优先选择轻量级模型，但多损失训练可能不稳定。常见策略是结合像素域和频率域目标；然而，低对比度、有限的高频内容以及传感器特有的噪声常导致它们的梯度对齐性弱或存在冲突。为解决这种优化歧义，我们提出正交梯度博弈与频率校正（OGG-FR），这是一种即插即用的优化框架，可将频率梯度分解为相对于像素梯度的冗余平行分量和正交创新分量。在冲突状态下，OGG-FR使用多梯度下降算法（MGDA）计算安全基梯度，并添加方差校正后的正交创新；在兼容状态下，它丢弃冗余平行信息，并根据从高频残差估计的置信度注入正交创新。在无人机热基准上的实验表明，在×4和×8尺度的BI和BD降质下均取得广泛提升，同时梯度分析支持所提出的感知冲突更新规则的有效性。
英文摘要：
Unmanned aerial vehicle (UAV) infrared image super-resolution aims to recover weak thermal structures for deployment on resource-constrained platforms; lightweight models are therefore preferred, but multi-loss training can be unstable. A common strategy combines pixel-domain and frequency-domain objectives; however, low contrast, limited high-frequency content, and sensor-specific noise often make their gradients weakly aligned or conflicting. To address this optimization ambiguity, we propose Orthogonal Gradient Gaming and Frequency Rectification (OGG-FR), a plug-and-play optimization framework that decomposes the frequency gradient into a redundant parallel component and an orthogonal innovation component relative to the pixel gradient. In the conflict regime, OGG-FR computes a safe base gradient using the Multiple Gradient Descent Algorithm (MGDA) and adds a variance-rectified orthogonal innovation; in the compatible regime, it discards redundant parallel information and injects the orthogonal innovation according to a confidence score estimated from the high-frequency residual. Experimental results on the UAV thermal benchmark show broad gains under BI and BD degradations at $\times 4$ and $\times 8$ scales, while gradient analyses support the effectiveness of the proposed conflict-aware update rule.
