Neural Network–Driven Algorithm for Efficient Noise Reduction and Edge Detection

Abstract

Image denoising and edge detection are fundamental challenges in image processing and computer vision, as noise significantly degrades image quality and complicates the accurate identification of structural features. Traditional filtering and gradient-based edge detection methods, while effective in controlled environments, often struggle with real-world noisy data, leading to blurred details and loss of critical edge information. This research proposes a neural network–based algorithm that integrates noise removal and edge detection into a unified framework, leveraging the adaptive learning capabilities of convolutional neural networks (CNNs). The model is designed to suppress various types of noise while preserving essential edges, ensuring improved visual clarity and structural consistency. Experimental evaluation demonstrates that the proposed approach achieves superior performance in terms of peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and edge accuracy when compared to conventional techniques. The study further highlights potential applications in medical imaging, remote sensing, surveillance, and industrial inspection.