A Review on: Image Restoration and Enhancement by Adapting CNN

Abstract

Image restoration and enhancement have become crucial in various fields, including healthcare, remote sensing, surveillance, and digital photography. Traditional approaches, such as filtering and wavelet transforms, have been widely used but often struggle with adaptability and generalization. With the advent of deep learning, particularly Convolutional Neural Networks (CNNs), image processing has witnessed a paradigm shift. CNN-based models excel at learning complex patterns and structures, enabling advanced tasks such as denoising, super-resolution, deblurring, and contrast enhancement. This review explores state-of-the-art CNN architectures and techniques employed in image restoration, analyzing their advantages, limitations, and real-world applications. Additionally, we discuss the challenges of CNN-based methods, including computational complexity and data requirements.