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Applied Sciences
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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: 31 December 2024 | Viewed by 4617

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

  • image enhancement
  • restoration techniques
  • adverse weather
  • image quality assessment

Published Papers (5 papers)

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Research

20 pages, 5032 KiB  
Article
Enhanced Learning Enriched Features Mechanism Using Deep Convolutional Neural Network for Image Denoising and Super-Resolution
by Iqra Waseem, Muhammad Habib, Eid Rehman, Ruqia Bibi, Rehan Mehmood Yousaf, Muhammad Aslam, Syeda Fizzah Jilani and Muhammad Waqar Younis
Appl. Sci. 2024, 14(14), 6281; https://doi.org/10.3390/app14146281 - 18 Jul 2024
Viewed by 361
Abstract
Image denoising and super-resolution play vital roles in imaging systems, greatly reducing the preprocessing cost of many AI techniques for object detection, segmentation, and tracking. Various advancements have been accomplished in this field, but progress is still needed. In this paper, we have [...] Read more.
Image denoising and super-resolution play vital roles in imaging systems, greatly reducing the preprocessing cost of many AI techniques for object detection, segmentation, and tracking. Various advancements have been accomplished in this field, but progress is still needed. In this paper, we have proposed a novel technique named the Enhanced Learning Enriched Features (ELEF) mechanism using a deep convolutional neural network, which makes significant improvements to existing techniques. ELEF consists of two major processes: (1) Denoising, which removes the noise from images; and (2) Super-resolution, which improves the clarity and details of images. Features are learned through deep CNN and not through traditional algorithms so that we can better refine and enhance images. To effectively capture features, the network architecture adopted Dual Attention Units (DUs), which align with the Multi-Scale Residual Block (MSRB) for robust feature extraction, working sidewise with the feature-matching Selective Kernel Extraction (SKF). In addition, resolution mismatching cases are processed in detail to produce high-quality images. The effectiveness of the ELEF model is highlighted by the performance metrics, achieving a Peak Signal-to-Noise Ratio (PSNR) of 42.99 and a Structural Similarity Index (SSIM) of 0.9889, which indicates the ability to carry out the desired high-quality image restoration and enhancement. Full article
(This article belongs to the Special Issue Advances in Image Enhancement and Restoration Technology)
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13 pages, 4733 KiB  
Article
Haze-Aware Attention Network for Single-Image Dehazing
by Lihan Tong, Yun Liu, Weijia Li, Liyuan Chen and Erkang Chen
Appl. Sci. 2024, 14(13), 5391; https://doi.org/10.3390/app14135391 - 21 Jun 2024
Viewed by 475
Abstract
Single-image dehazing is a pivotal challenge in computer vision that seeks to remove haze from images and restore clean background details. Recognizing the limitations of traditional physical model-based methods and the inefficiencies of current attention-based solutions, we propose a new dehazing network combining [...] Read more.
Single-image dehazing is a pivotal challenge in computer vision that seeks to remove haze from images and restore clean background details. Recognizing the limitations of traditional physical model-based methods and the inefficiencies of current attention-based solutions, we propose a new dehazing network combining an innovative Haze-Aware Attention Module (HAAM) with a Multiscale Frequency Enhancement Module (MFEM). The HAAM is inspired by the atmospheric scattering model, thus skillfully integrating physical principles into high-dimensional features for targeted dehazing. It picks up on latent features during the image restoration process, which gives a significant boost to the metrics, while the MFEM efficiently enhances high-frequency details, thus sidestepping wavelet or Fourier transform complexities. It employs multiscale fields to extract and emphasize key frequency components with minimal parameter overhead. Integrated into a simple U-Net framework, our Haze-Aware Attention Network (HAA-Net) for single-image dehazing significantly outperforms existing attention-based and transformer models in efficiency and effectiveness. Tested across various public datasets, the HAA-Net sets new performance benchmarks. Our work not only advances the field of image dehazing but also offers insights into the design of attention mechanisms for broader applications in computer vision. Full article
(This article belongs to the Special Issue Advances in Image Enhancement and Restoration Technology)
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18 pages, 15807 KiB  
Article
Enhanced Solar Coronal Imaging: A GAN Approach with Fused Attention and Perceptual Quality Enhancement
by Zhenhong Shang and Ruiyao Li
Appl. Sci. 2024, 14(10), 4054; https://doi.org/10.3390/app14104054 - 10 May 2024
Viewed by 650
Abstract
The activity of the solar corona has a significant impact on all aspects of human life. People typically use images obtained from astronomical telescopes to observe coronal activities, among which the Atmospheric Imaging Assembly (AIA) of the Solar Dynamics Observatory (SDO) is particularly [...] Read more.
The activity of the solar corona has a significant impact on all aspects of human life. People typically use images obtained from astronomical telescopes to observe coronal activities, among which the Atmospheric Imaging Assembly (AIA) of the Solar Dynamics Observatory (SDO) is particularly widely used. However, due to resolution limitations, we have begun to study the application of generative adversarial network super-resolution techniques to enhance the image data quality for a clearer observation of the fine structures and dynamic processes in the solar atmosphere, which improves the prediction accuracy of solar activities. We aligned SDO/AIA images with images from the High-Resolution Coronal Imager (Hi-C) to create a dataset. This research proposes a new super-resolution method named SAFCSRGAN, which includes a spatial attention module that incorporates channel information, allowing the network model to better capture the corona’s features. A Charbonnier loss function was introduced to enhance the perceptual quality of the super-resolution images. Compared to the original method using ESRGAN, our method achieved an 11.9% increase in Peak Signal-to-Noise Ratio (PSNR) and a 4.8% increase in Structural Similarity (SSIM). Additionally, we introduced two perceptual image quality assessment metrics, the Natural Image Quality Evaluator (NIQE) and Learned Perceptual Image Patch Similarity (LPIPS), which improved perceptual quality by 10.8% and 1.3%, respectively. Finally, our experiments demonstrated that our improved model surpasses other models in restoring the details of coronal images. Full article
(This article belongs to the Special Issue Advances in Image Enhancement and Restoration Technology)
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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
Cited by 3 | Viewed by 1338
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 885
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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