There has been considerable progress in implicit neural representation to upscale an image to any arbitrary resolution. However, existing methods are based on defining a function to predict the Red, Green and Blue (RGB) value from just four specific loci. Relying on just four loci is insufficient as it leads to losing fine details from the neighboring region(s). We show that by taking into account the semi-local region leads to an improvement in performance. In this paper, we propose applying a new technique called Overlapping Windows on Semi-Local Region (OW-SLR) to an image to obtain any arbitrary resolution by taking the coordinates of the semi-local region around a point in the latent space. This extracted detail is used to predict the RGB value of a point. We illustrate the technique by applying the algorithm to the Optical Coherence Tomography-Angiography (OCT-A) images and show that it can upscale them to random resolution. This technique outperforms the existing state-of-the-art methods when applied to the OCT500 dataset. OW-SLR provides better results for classifying healthy and diseased retinal images such as diabetic retinopathy and normals from the given set of OCT-A images. The project page is available at https://rishavbb.github.io/ow-slr/index.html

翻译：隐式神经表示在将图像放大至任意分辨率方面取得了显著进展。然而，现有方法基于仅从四个特定坐标点预测红绿蓝（RGB）值的函数定义。仅依赖四个坐标点存在不足，会导致邻域区域细节丢失。研究表明，引入半局部区域可提升性能。本文提出一种名为"半局部区域重叠窗口"（OW-SLR）的新技术，通过提取潜在空间中某点周围半局部区域的坐标信息，实现图像的任意分辨率重建。该提取的细节信息用于预测该点的RGB值。我们通过将该算法应用于光学相干断层扫描血管成像（OCT-A）图像进行技术验证，证明其可实现随机分辨率放大。在OCT500数据集上，该技术性能优于现有最先进方法。OW-SLR能更好地区分健康与病变视网膜图像（如糖尿病视网膜病变与正常样本）。项目页面详见https://rishavbb.github.io/ow-slr/index.html