A Comprehensive Review of Deep Learning-Based Real-World Image Restoration

Real-world imagery does not always exhibit good visibility and clean content, but often suffers from various kinds of degradations (e.g., noise, blur, rain drops, fog, color distortion, etc.), which severely affect vision-driven tasks (e.g., image classification, target recognition, and tracking, et...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Zhai, Lujun, Wang, Yonghui, Cui, Suxia, Zhou, Yu
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
benchmark datasets
Benchmark testing
Computer architecture
deblurring
Deep learning
Degradation
dehazing
denoising
deraining
Generative adversarial networks
Image classification
Image color analysis
Image processing
Image quality
image quality assessment
Image restoration
Machine learning
Multilayer perceptrons
Multilayers
Neural networks
Quality assessment
Raindrops
review
super-resolution
Target recognition
Task analysis
Tracking
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Summary:Real-world imagery does not always exhibit good visibility and clean content, but often suffers from various kinds of degradations (e.g., noise, blur, rain drops, fog, color distortion, etc.), which severely affect vision-driven tasks (e.g., image classification, target recognition, and tracking, etc.). Thus, restoring the true scene from such degraded images is of significance. In recent years, a large body of deep learning-based image processing works has been exploited due to the advances in deep neural networks. This paper aims to make a comprehensive review of real-world image restoration algorithms and beyond. More specifically, this review provides overviews of critical benchmark datasets, image quality assessment methods, and four major categories of deep learning-based image restoration methods, i.e., based on convolutional neural network (CNN), generative adversarial network (GAN), Transformer, and multi-layer perceptron (MLP). The paper highlights the latest developments and advances in each category of network architecture to provide an up-to-date overview. Moreover, the representative state-of-the-art image restoration methods are compared visually and numerically. Finally, for real-world image restoration, the current situations are objectively assessed, challenges are discussed, and future directions and trends are presented.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3250616