Image Denoising Using Deep Learning and Wavelet Transform

Image denoising is a critical task in image processing, aimed at improving visual quality by removing unwanted noise introduced during acquisition, transmission, or storage. Noise can degrade visual content and affect image analysis tasks such as edge detection, object recognition, and feature extraction. As digital images are widely used in fields like medical imaging, satellite imaging, and photography, developing efficient denoising techniques is essential. The core challenge lies in balancing noise reduction with detail preservation. Traditional methods like Gaussian and median filtering often blur important features. This project integrates Convolutional Neural Networks (CNNs) with wavelet transforms, leveraging multi-resolution analysis to separate noise from image features. CNNs further refine the output by learning complex patterns for enhanced denoising. By isolating noise through wavelet coefficients and reconstructing clean images using CNNs, this hybrid approach achieves high-quality denoising with minimal detail loss. This project seeks to advance image processing by offering a robust solution that balances noise reduction with detail preservation.