只提升图像局部区域分辨率，节省算力且效果更好
LocalSR: Image Super-Resolution in Local Region
- 按需修复指定区域，利用全图上下文信息增强细节
- 三模块并行处理，远距离像素特征逐步传至目标区
- 计算量更低，对人脸、车牌等关键区域更高效
标准单图像超分辨率（SR）对整张图像进行上采样和修复。但在实际应用中，如车牌、人脸等特定区域需要更高分辨率，整体图像处理带来不必要的内存与计算开销。本文提出新任务LocalSR，仅恢复低分辨率图像中的局部感兴趣区域（ROI）。为此，设计基于上下文的局部超分辨率（CLSR）方法：通过三个并行模块——基础模块负责修复ROI，全局上下文模块从全图提取有用特征，邻近融合模块聚焦于ROI周围区域，逐步将远距离像素特征传递至目标区域。实验表明，该方法在保持低复杂度的前提下，优于仅聚焦于ROI的基线模型。
原文摘要 · Abstract (English)
Standard single-image super-resolution (SR) upsamples and restores entire images. Yet several real-world applications require higher resolutions only in specific regions, such as license plates or faces, making the super-resolution of the entire image, along with the associated memory and computational cost, unnecessary. We propose a novel task, called LocalSR, to restore only local regions of the low-resolution image. For this problem setting, we propose a context-based local super-resolution (CLSR) to super-resolve only specified regions of interest (ROI) while leveraging the entire image as context. Our method uses three parallel processing modules: a base module for super-resolving the ROI, a global context module for gathering helpful features from across the image, and a proximity integration module for concentrating on areas surrounding the ROI, progressively propagating features from distant pixels to the target region. Experimental results indicate that our approach, with its reduced low complexity, outperforms variants that focus exclusively on the ROI.
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