In medical image processing, the automatic analysis of pathology localization and the anatomical segmentation steps are more important. The Fundus images of Low resolution (LR) are not applicable to detect the retinal disease. The main aim of this paper is to enhance the resolution of the low-resolution retinal images obtained from the cheap imaging devices within less computational time and high accuracy. So, we proposed the fundus image with Super-Resolution and its performance via the Diagnostically Significant Area (DSA). This approach focuses only on the region of Interest (ROI) instead of concentrating on the entire image leading to less computational time by reducing the time complexity. Therefore, the Eigen MR inter-band feature, Energy MR intra-band feature, Shannon entropy and Sensitive Contrast Interest (SCI) are used to capture the clinical data from the selected region. Therefore, the DSA is determined by using Levenshtein based KNN classifier. Because of better classification outcomes, the Bicubic method is employed in the selected region to reduce the loss of reconstruction error. Experimentally, the implementation works are carried out in the platform of MATLAB with DRIVE and STARE database images are chosen. The super-resolution image performances are compared with different start of art techniques such as PSM, GR-SR, LLE, and SpC-SR. Finally, higher efficiency with low computational super-resolution fundus images is collected.

在医学图像处理中，病理定位和解剖分割步骤的自动分析更为重要。低分辨率（LR）的眼底图像不适用于检测视网膜疾病。本文的主要目的是在更短的计算时间和更高的精度下提高从廉价成像设备获得的低分辨率视网膜图像的分辨率。因此，我们通过诊断重要区域（DSA）提出了具有超分辨率的眼底图像及其性能。这种方法仅专注于感兴趣区域（ROI），而不是专注于整个图像，从而通过减少时间复杂度而减少了计算时间。因此，本征MR带间功能，能量MR带内功能，香农熵和敏感对比兴趣（SCI）用于捕获所选区域的临床数据。因此，通过使用基于Levenshtein的KNN分类器来确定DSA。由于更好的分类结果，在选定区域中采用了Bicubic方法以减少重建误差的损失。实验上，在MATLAB平台上进行了执行工作，并选择了DRIVE和STARE数据库图像。将超分辨率图像性能与诸如PSM，GR-SR，LLE和SpC-SR等不同的现有技术进行了比较。最后，以较低的计算超分辨率眼底图像获得更高的效率。在选择的区域中采用Bicubic方法以减少重建误差的损失。实验上，在MATLAB平台上进行了执行工作，并选择了DRIVE和STARE数据库图像。将超分辨率图像性能与诸如PSM，GR-SR，LLE和SpC-SR等不同的现有技术进行了比较。最后，以较低的计算超分辨率眼底图像获得更高的效率。在选择的区域中采用Bicubic方法以减少重建误差的损失。实验上，在MATLAB平台上进行了执行工作，并选择了DRIVE和STARE数据库图像。将超分辨率图像性能与诸如PSM，GR-SR，LLE和SpC-SR等不同的现有技术进行了比较。最后，以较低的计算超分辨率眼底图像获得更高的效率。