The technique of determining the borders between several objects or regions in an image is known as edge detection. The edges of an object in an image serve as the object’s limits and can reveal crucial details about the object’s size, shape, and position. The pre-processing stage of edge detectionis crucial because it can increase the precision and effectiveness of edge detection algorithms. As low-density or low-pixel values muddy the image, detecting edges in low-resolution images is difficult. This paper aims to introduce LRED, an improved edge detection model for low-resolution images based on Gaussian smoothing. Also used for image pre-processing and smoothing is the Gaussian filter. The Gaussian smoothing method works well for spotting edges in images. Additionally, we have presented a comprehensive comparison of our proposed approach with three modern, cutting-edge detection approaches and algorithms. Investigations have been conducted on several images in addition to low-quality images to discover edges. RMSE and PSNR are two different evaluation metrics used to measure proposed methods. LRED achieved 90.25% MSE, which is slightly better than the other three approaches which show more reliable outcomes.

中文翻译：

---

低分辨率图像的增强边缘检测模型

确定图像中多个对象或区域之间的边界的技术称为边缘检测。图像中对象的边缘充当对象的界限，可以揭示有关对象大小、形状和位置的重要细节。边缘检测的预处理阶段至关重要，因为它可以提高边缘检测算法的精度和有效性。由于低密度或低像素值使图像变得模糊，因此检测低分辨率图像中的边缘很困难。本文旨在介绍LRED，一种基于高斯平滑的改进的低分辨率图像边缘检测模型。高斯滤波器也用于图像预处理和平滑。高斯平滑方法非常适合发现图像中的边缘。此外，我们还对我们提出的方法与三种现代尖端检测方法和算法进行了全面比较。除了低质量图像之外，还对多张图像进行了调查以发现边缘。RMSE 和 PSNR 是用于衡量所提出方法的两种不同的评估指标。LRED 的 MSE 达到了 90.25%，略好于其他三种方法，显示出更可靠的结果。