Super-Resolution (SISR) is a computer vision task that aims to generate high-resolution images from their low-resolution counterparts. Typically, Super-Resolution methods use scaling factors of x2, x3, or x4 to uniformly enhance the resolution of the entire image. However, some acquisition devices, such as 360 \(^{\circ }\) cameras, produce images with non-uniform resolution across the frame. In this work, we propose to adapt state-of-the-art efficient methods for Single Image Super-Resolution to address the challenge of restoring images affected by spatially varying degradations. Specifically, we focus on the method that won the recent NTIRE 2024 Efficient Super-Resolution Challenge. For our experiments, synthetic images with different spatially varying types of degradation are generated, and the SISR method is specifically modified and trained to effectively handle such challenging scenarios. In addition to evaluating the developed method with traditional image quality metrics such as PSNR and SSIM, we also assess its practical impact on a downstream object detection task. The results on the WIDER FACE face-detection dataset, using the YOLOv8 object detection model, show that applying the proposed SISR approach to images with spatially varying degradations produces artifact-free outputs and enables object detectors to achieve superior performance compared to their application on degraded images.