Image restoration using deep learning techniques: A comprehensive review
Abstract
Image restoration plays a vital role in computer vision by enhancing degraded images. Deep learning techniques, including convolutional neural networks and generative adversarial networks, have significantly advanced this field. The key tasks include deblurring, inpainting, denoising, and super-resolution. This study reviews current restoration methods and compares their strengths and limitations across various applications. It identifies areas for improvement and recommends suitable techniques for specific tasks. The applications span healthcare, underwater imaging, and the preservation of old photographs. Enhanced restoration enables better interpretation, storage, and usability of visual data across diverse domains.
Fields of Study & Subjects
Advanced Image Processing Techniques
Brain Tumor Detection and Classification
Generative Adversarial Networks and Image Synthesis
Publication details
- Journal / source
- ICT Express
- Publisher
- Elsevier BV
- Volume
- 12
- Issue
- 3
- Pages
- 593–609
- Publication date
- 5 March 2026
- Language
- EN