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：在隐式神经表示实现图像任意尺度放大的研究中已取得显著进展。然而，现有方法仅通过四个特定坐标点定义函数预测红、绿、蓝（RGB）值。仅依赖四个坐标点存在不足，因其会导致邻近区域的细节信息丢失。我们证明，引入半局部区域可提升性能。本文提出一种名为"基于半局部区域的重叠窗口"（OW-SLR）的新技术，该方法通过提取潜空间中某点周围半局部区域的坐标信息，实现图像任意分辨率重建。提取的细节信息被用于预测该点的RGB值。我们通过将该算法应用于光学相干断层扫描血管造影（OCT-A）图像验证其有效性，实验表明该技术可将其放大至随机分辨率。在OCT500数据集上，该方法的性能超越了现有主流技术。此外，OW-SLR在OCT-A图像数据集上对糖尿病视网膜病变等健康与病变视网膜图像（含正常样本）的分类任务中展现出更优结果。项目页面详见https://rishavbb.github.io/ow-slr/index.html