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Applied Sciences
Special Issues
Advances in Image Enhancement and Restoration Technology
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Advances in Image Enhancement and Restoration Technology

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

Deadline for manuscript submissions: 20 June 2024 | Viewed by 2220

Special Issue Editors

Prof. Dr. Xiaofang Hu

E-Mail Website
Guest Editor
College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Interests: image processing; memristive neural network; deep learning
Dr. Yun Liu

E-Mail Website
Guest Editor
College of Artificial Intelligence, Southwest University, Chongqing 400715, China
Interests: image enhancement; image restoration; deep learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computer vision has extensive applications in various fields, including areas such as outdoor navigation, security surveillance, object detection, underwater exploration, and target recognition. Possessing a high-quality clear image is crucial if computer vision systems are to obtain accurate visual information. However, under various complex imaging conditions, such as fog, haze, rain, snow, low-light and underwater environments, the acquired images suffer from severe color distortion, scene blurring, and poor clarity, significantly impacting their applications and restricting related research in these fields.

The goal of this Special Issue is to explore the recent advances in the field of image enhancement and restoration. Enhancing and restoring low-quality images affected by adverse weather or other conditions is crucial for achieving accurate and reliable visual information extraction. This Special Issue aims to bring together researchers and experts to share their innovative approaches, methodologies, and findings in addressing the challenges and advancing the state of the art in this field.

Prof. Dr. Xiaofang Hu
Dr. Yun Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • hazy image enhancement and restoration techniques
  • snowy image enhancement and restoration techniques
  • rainy image enhancement and restoration techniques
  • low-light image enhancement and restoration technologies
  • underwater image enhancement and restoration techniques
  • remote sensing image enhancement and restoration algorithms
  • all-in-one adverse weather removal algorithms
  • image quality assessment

Published Papers (2 papers)

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Research

15 pages, 1091 KiB  
Article
Swin-APT: An Enhancing Swin-Transformer Adaptor for Intelligent Transportation
by Yunzhuo Liu, Chunjiang Wu, Yuting Zeng, Keyu Chen and Shijie Zhou
Appl. Sci. 2023, 13(24), 13226; https://doi.org/10.3390/app132413226 - 13 Dec 2023
Viewed by 914
Abstract
Artificial Intelligence has been widely applied in intelligent transportation systems. In this work, Swin-APT, a deep learning-based approach for semantic segmentation and object detection in intelligent transportation systems is presented. Swin-APT includes a lightweight network and a multiscale adapter network designed for image [...] Read more.
Artificial Intelligence has been widely applied in intelligent transportation systems. In this work, Swin-APT, a deep learning-based approach for semantic segmentation and object detection in intelligent transportation systems is presented. Swin-APT includes a lightweight network and a multiscale adapter network designed for image semantic segmentation and object detection tasks. An inter-frame consistency module is proposed to extract more accurate road information from images. Experimental results on four datasets: BDD100K, CamVid, SYNTHIA, and CeyMo, demonstrate that Swin-APT outperforms the baseline by 13.1%. Furthermore, experiments on the road marking detection benchmark show an improvement of 1.85% of mAcc. Full article
(This article belongs to the Special Issue Advances in Image Enhancement and Restoration Technology)
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22 pages, 32278 KiB  
Article
LPS-Net: Lightweight Parallel Strategy Network for Underwater Image Enhancement
by Jingxia Jiang, Peiyun Huang, Lihan Tong, Junjie Yin and Erkang Chen
Appl. Sci. 2023, 13(16), 9419; https://doi.org/10.3390/app13169419 - 19 Aug 2023
Viewed by 695
Abstract
Underwater images are frequently subject to color distortion and loss of details. However, previous enhancement methods did not tackle these mixed degradations by dividing them into sub-problems that could be effectively addressed. Moreover, the parameters and computations required for these methods are usually [...] Read more.
Underwater images are frequently subject to color distortion and loss of details. However, previous enhancement methods did not tackle these mixed degradations by dividing them into sub-problems that could be effectively addressed. Moreover, the parameters and computations required for these methods are usually costly for underwater equipment, which has limited power supply, processing capabilities, and memory capacity. To address these challenges, this work proposes a Lightweight Parallel Strategy Network (LPS-Net). Firstly, a Dual-Attention Enhancement Block and a Mirror Large Receptiveness Block are introduced to, respectively, enhance the color and restore details in degraded images. Secondly, we employed these blocks on parallel branches at each stage of LPS-Net, with the goal of achieving effective image color and detail rendering simultaneously. Thirdly, a Gated Fusion Unit is proposed to merge features from different branches at each stage. Finally, the network utilizes four stages of parallel enhancement, achieving a balanced trade-off between performance and parameters. Extensive experiments demonstrated that LPS-Net achieves optimal color enhancement and superior detail restoration in terms of visual quality. Furthermore, it attains state-of-the-art underwater image enhancement performance on the evaluation metrics, while using only 80.12 k parameters. Full article
(This article belongs to the Special Issue Advances in Image Enhancement and Restoration Technology)
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