A Unified Deep Learning Framework for Image Restoration: Applications to Inpainting, Super-Resolution, Deblurring, and Denoising

Abstract

The advent of deep learning has revolutionized the field of image restoration, providing significant advancements in areas such as inpainting, super-resolution, deblurring, and denoising. Image restoration techniques aim to recover a clean and high-quality image from its corrupted version, and the use of deep learning models has proven to be highly effective in improving restoration outcomes. Inpainting focuses on filling missing or damaged regions in an image, while super-resolution seeks to enhance image resolution, resulting in more detailed and clear images. Deblurring aims to remove blurring effects caused by camera motion or defocus, and denoising removes unwanted noise from images. Traditional methods in image restoration, such as filtering and interpolation techniques, have limitations in terms of flexibility, scalability, and the ability to handle complex image distortions. Deep learning, with its ability to learn from large datasets and generalize across diverse image types, has emerged as a powerful tool in overcoming these limitations. Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) have been particularly instrumental in these developments. This paper provides an in-depth review of recent advancements in deep learning-based image restoration techniques, highlighting key approaches, challenges, and future directions. The paper also explores the underlying architectures, datasets used for training models, evaluation metrics, and the comparative performance of deep learning models against traditional methods. Additionally, we discuss the applications of image restoration in real-world scenarios, such as medical imaging, satellite imaging, and multimedia content enhancement. Finally, the paper concludes by providing insights into the future of deep learning for image restoration, considering ongoing challenges, such as model generalization, computational cost, and interpretability.

DOI - https://doi.org/10.65525/SVUP.9788199778009.2026.201-207