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Deep Learning and Transformer Technologies for Image/Video Enhancement and Restoration

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 February 2025) | Viewed by 1566

Special Issue Editors


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Guest Editor
Department of Automation, University of Science and Technology of China, Hefei 230026, China
Interests: image enhancement and restoration; computer vision; machine learning; deep neural networks

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Guest Editor
School of Computer Science and Technology and Ministry of Education Key Lab of Intelligent Network Security, Xi’an Jiaotong University, Xi’an 710049, China
Interests: image restoration; image fusion; statistical machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As we experience technological advancements like intelligent terminals, multimedia, and the internet, visual systems reliant on image/video data have been extensively applied across various industries. These include, but are not limited to, remote sensing mapping, security monitoring, and autonomous driving. However, in many real-world scenarios, image/video data generation and acquisition processes are susceptible to disruption, leading to considerable quality degradation. Therefore, image/video enhancement and restoration are critical processes in the computer vision field. In recent years, deep learning and Transformer technologies have emerged as solutions in computer vision, offering unprecedented improvements in image and video data quality. This Special Issue seeks original contributions that aim to advance the theory, architecture, and algorithmic design for deep learning, Transformer models in image/video enhancement and restoration, and novel applications of these technologies.

Dr. Xueyang Fu
Dr. Xiangyong Cao
Guest Editors

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Keywords

  • image/video enhancement
  • denoising
  • super-resolution
  • deblurring
  • dehazing
  • inpainting
  • deraining
  • deep learning
  • transformer

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Published Papers (1 paper)

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Research

16 pages, 8462 KiB  
Article
Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
by Hua Wang, Pornntiwa Pawara and Rapeeporn Chamchong
Appl. Sci. 2025, 15(3), 1311; https://doi.org/10.3390/app15031311 - 27 Jan 2025
Viewed by 786
Abstract
Video deblurring faces a fundamental challenge, as blur degradation comprehensively affects frames by not only causing detail loss but also severely distorting structural information. This dual degradation across low- and high-frequency domains makes it challenging for existing methods to simultaneously restore both structural [...] Read more.
Video deblurring faces a fundamental challenge, as blur degradation comprehensively affects frames by not only causing detail loss but also severely distorting structural information. This dual degradation across low- and high-frequency domains makes it challenging for existing methods to simultaneously restore both structural and detailed information through a unified approach. To address this issue, we propose a wavelet-based, blur-aware decoupled network (WBDNet) that innovatively decouples structure reconstruction from detail enhancement. Our method decomposes features into multiple frequency bands and employs specialized restoration strategies for different frequency domains. In the low-frequency domain, we construct a multi-scale feature pyramid with optical flow alignment. This enables accurate structure reconstruction through bottom-up progressive feature fusion. For high-frequency components, we combine deformable convolution with a blur-aware attention mechanism. This allows us to precisely extract and merge sharp details from multiple frames. Extensive experiments on benchmark datasets demonstrate the superior performance of our method, particularly in preserving structural integrity and detail fidelity. Full article
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