Image restoration is a critical area in computer vision and image processing, aiming to recover high-quality images from degraded ones. Traditional image restoration techniques have made significant strides, but the advent of deep learning has revolutionized this field. Deep learning models, particularly convolutional neural networks (CNNs), have shown impressive performance in restoring images corrupted by noise, blur, or other distortions. This paper explores the role of deep learning in image restoration, reviewing various models and architectures, highlighting their strengths and weaknesses, and presenting potential future directions for research. By comparing deep learning-based methods with traditional techniques, this paper aims to showcase the effectiveness and efficiency of deep learning in solving image restoration challenges. Keywords: Image restoration, deep learning, convolutional neural networks (CNN), image denoising, image deblurring, image inpainting, generative models, super-resolution