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.

翻译：标准的单图像超分辨率（SR）方法对整个图像进行上采样和恢复。然而，一些实际应用仅需对特定区域（如车牌或人脸）进行更高分辨率的重建，这使得对整个图像进行超分辨率处理及其伴随的内存和计算成本变得不必要。我们提出了一种称为LocalSR的新任务，旨在仅恢复低分辨率图像的局部区域。针对此问题设定，我们提出了一种基于上下文的局部超分辨率（CLSR）方法，以仅对指定的感兴趣区域（ROI）进行超分辨率重建，同时利用整个图像作为上下文。我们的方法采用三个并行处理模块：一个用于超分辨率重建ROI的基础模块，一个用于从整个图像收集有用特征的全局上下文模块，以及一个专注于ROI周围区域的邻近集成模块，该模块逐步将远处像素的特征传播至目标区域。实验结果表明，我们提出的方法在降低复杂度的同时，其性能优于仅专注于ROI的变体方法。