Smart Image Denoising System Using Deep Learning Techniques
Author : Dr. K Kalyani and V Rajeshwari
Abstract :
Image denoising is a fundamental task in digital image processing aimed at removing unwanted noise while preserving essential image details and textures. This paper presents a deep learning-based smart image denoising system using Convolutional Neural Networks (CNNs), particularly the Denoising Convolutional Neural Network (DnCNN). The proposed approach effectively handles different noise types such as Gaussian, salt-and-pepper, and speckle noise. A residual learning strategy is employed to predict noise components, enabling efficient noise removal while maintaining structural integrity. Experimental evaluations demonstrate significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) compared to traditional denoising methods. The proposed system is suitable for applications in photography, medical imaging, and remote sensing.
Keywords :
Image Denoising, Deep Learning, Convolutional Neural Network (CNN), DnCNN, GAN, PSNR, SSIM, Noise Reduction, Image Enhancement.
