Paper Contents
Abstract
Image denoising is the process of removing noise from an image while preserving important details like edges, textures, and structures. Noise in an image typically arises from factors such as low-light conditions, sensor limitations, or transmission errors. Deep learning is a subset of machine learning, which itself is a branch of artificial intelligence (AI). It uses artificial neural networks, particularly deep neural networks, to learn patterns, make predictions, and solve complex tasks. Image denoising and deep learning are closely linked due to deep learning techniques like Convolutional Neural Networks (CNNs) and autoencoders. These models effectively remove noise from images, learning complex noise patterns from large datasets. They can adapt to Gaussian and salt-and-pepper noises while preserving image details. The Matrix Factorization Denoising Module (MFDM) and Feature Fusion Module (FFM) are advanced techniques in deep learning-based image denoising. MFDM decomposes noisy images into low-rank matrix and sparse noise components, while FFM captures fine details and global structures. These modules balance noise removal and detail preservation, improving denoising performance, especially in complex and high-noise environments. Deep learning techniques like CNNs, GANS, MFDM, and FFM improve image quality in fields like photography, medical imaging, and surveillance by cleaning up noisy images and preserving essential details, resulting in clearer visuals and enhanced real-world applications.
Copyright
Copyright © 2024 PALIVELA SRI HARSHINI. This is an open access article distributed under the Creative Commons Attribution License.
