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Image Denoising and Image Super-resolution for Sensing Application
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Image Denoising and Image Super-resolution for Sensing Application

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 12852

Special Issue Editors

Dr. Kai Zhang

E-Mail Website
Guest Editor
Department of Information Technology and Electrical Engineering, ETH Zurich, ETH Zentrum, 8092 Zurich, Switzerland
Interests: image processing; deep plug-and-play image restoration; deep unfolding image restoration; camera pipeline; blind image restoration
Dr. Dongwei Ren

E-Mail Website
Guest Editor
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Interests: Image and video restoration; object detection and segmentation

Special Issue Information

Dear Colleagues,

Due to the various factors during the image acquisition and transmission process, such as the poor imaging system, storage and bandwidth limitation, and insufficient computational power, the RAW sensor data and the processed images are often corrupted by noise and have low spatial resolution. Image denoising and image super-resolution, as two classical and yet active low-level vision research topics, can be applied on the RAW sensor data and the processed images to improve the image quality and the accuracy of subsequent high-level vision tasks. This Special Issue will present recent advances of image denoising and image super-resolution in sensing applications. Specifically, novel model-based methods, learning-based methods, or hybrid methods such as plug-and-play methods and deep unfolding methods for image denoising and image super-resolution will be of special attention.

Dr. Kai Zhang
Dr. Dongwei Ren
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. Sensors 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 2600 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 denoising
  • image super-resolution
  • image demosaicing, deblurring, dehazing and de-raining
  • image compression artifacts reduction
  • deep plug-and-play image restoration
  • deep unfolding image restoration
  • learned image signal processing (ISP) pipeline
  • the combination of image restoration and high-level vision tasks
  • efficient deep architecture design for image denoising and image super-resolution
  • image restoration based on multi-sensor data

Published Papers (6 papers)

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Research

16 pages, 2377 KiB  
Article
Image-Processing-Based Subway Tunnel Crack Detection System
by Xiaofeng Liu, Zenglin Hong, Wei Shi and Xiaodan Guo
Sensors 2023, 23(13), 6070; https://doi.org/10.3390/s23136070 - 30 Jun 2023
Cited by 2 | Viewed by 1205
Abstract
With the increase in urban rail transit construction, instances of tunnel disease are on the rise, and cracks have become the focus of tunnel maintenance and management. Therefore, it is essential to carry out crack detection in a timely and efficient manner to [...] Read more.
With the increase in urban rail transit construction, instances of tunnel disease are on the rise, and cracks have become the focus of tunnel maintenance and management. Therefore, it is essential to carry out crack detection in a timely and efficient manner to not only prolong the service life of the tunnel but also reduce the incidence of accidents. In this paper, the design and structure of a tunnel crack detection system are analyzed. On this basis, this paper proposes a new method for crack identification and feature detection using image processing technology. This method fully considers the characteristics of tunnel images and the combination of these characteristics with deep learning, while a deep convolutional network (Single-Shot MultiBox Detector (SSD)) is proposed based on deep learning for object detection in complex images. The experimental results show that the test set accuracy and training set accuracy of the support vector machine (SVM) in the classification comparison test are up to 88% and 87.8%, respectively; while the test accuracy of Alexnet’s deep convolutional neural network-based classification and identification is up to 96.7%, and the training set accuracy is up to 97.5%. It can be seen that this deep convolutional network recognition algorithm based on deep learning and image processing is better and more suitable for the detection of cracks in subway tunnels. Full article
(This article belongs to the Special Issue Image Denoising and Image Super-resolution for Sensing Application)
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21 pages, 9030 KiB  
Article
Reparameterizable Multibranch Bottleneck Network for Lightweight Image Super-Resolution
by Ying Shen, Weihuang Zheng, Feng Huang, Jing Wu and Liqiong Chen
Sensors 2023, 23(8), 3963; https://doi.org/10.3390/s23083963 - 13 Apr 2023
Cited by 2 | Viewed by 1539
Abstract
Deployment of deep convolutional neural networks (CNNs) in single image super-resolution (SISR) for edge computing devices is mainly hampered by the huge computational cost. In this work, we propose a lightweight image super-resolution (SR) network based on a reparameterizable multibranch bottleneck module (RMBM). [...] Read more.
Deployment of deep convolutional neural networks (CNNs) in single image super-resolution (SISR) for edge computing devices is mainly hampered by the huge computational cost. In this work, we propose a lightweight image super-resolution (SR) network based on a reparameterizable multibranch bottleneck module (RMBM). In the training phase, RMBM efficiently extracts high-frequency information by utilizing multibranch structures, including bottleneck residual block (BRB), inverted bottleneck residual block (IBRB), and expand–squeeze convolution block (ESB). In the inference phase, the multibranch structures can be combined into a single 3 × 3 convolution to reduce the number of parameters without incurring any additional computational cost. Furthermore, a novel peak-structure-edge (PSE) loss is proposed to resolve the problem of oversmoothed reconstructed images while significantly improving image structure similarity. Finally, we optimize and deploy the algorithm on the edge devices equipped with the rockchip neural processor unit (RKNPU) to achieve real-time SR reconstruction. Extensive experiments on natural image datasets and remote sensing image datasets show that our network outperforms advanced lightweight SR networks regarding objective evaluation metrics and subjective vision quality. The reconstruction results demonstrate that the proposed network can achieve higher SR performance with a 98.1 K model size, which can be effectively deployed to edge computing devices. Full article
(This article belongs to the Special Issue Image Denoising and Image Super-resolution for Sensing Application)
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12 pages, 1601 KiB  
Article
SAR Image Ship Target Detection Adversarial Attack and Defence Generalization Research
by Wei Gao, Yunqing Liu, Yi Zeng, Quanyang Liu and Qi Li
Sensors 2023, 23(4), 2266; https://doi.org/10.3390/s23042266 - 17 Feb 2023
Cited by 1 | Viewed by 1295
Abstract
The synthetic aperture radar (SAR) image ship detection system needs to adapt to an increasingly complicated actual environment, and the requirements for the stability of the detection system continue to increase. Adversarial attacks deliberately add subtle interference to input samples and cause models [...] Read more.
The synthetic aperture radar (SAR) image ship detection system needs to adapt to an increasingly complicated actual environment, and the requirements for the stability of the detection system continue to increase. Adversarial attacks deliberately add subtle interference to input samples and cause models to have high confidence in output errors. There are potential risks in a system, and input data that contain confrontation samples can be easily used by malicious people to attack the system. For a safe and stable model, attack algorithms need to be studied. The goal of traditional attack algorithms is to destroy models. When defending against attack samples, a system does not consider the generalization ability of the model. Therefore, this paper introduces an attack algorithm which can improve the generalization of models by based on the attributes of Gaussian noise, which is widespread in actual SAR systems. The attack data generated by this method have a strong effect on SAR ship detection models and can greatly reduce the accuracy of ship recognition models. While defending against attacks, filtering attack data can effectively improve the model defence capabilities. Defence training greatly improves the anti-attack capacity, and the generalization capacity of the model is improved accordingly. Full article
(This article belongs to the Special Issue Image Denoising and Image Super-resolution for Sensing Application)
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18 pages, 5071 KiB  
Article
Blind Watermarking for Hiding Color Images in Color Images with Super-Resolution Enhancement
by Hwai-Tsu Hu, Ling-Yuan Hsu and Shyi-Tsong Wu
Sensors 2023, 23(1), 370; https://doi.org/10.3390/s23010370 - 29 Dec 2022
Cited by 3 | Viewed by 2113
Abstract
This paper presents a novel approach for directly hiding the pixel values of a small color watermark in a carrier color image. Watermark embedding is achieved by modulating the gap of paired coefficient magnitudes in the discrete cosine transform domain according to the [...] Read more.
This paper presents a novel approach for directly hiding the pixel values of a small color watermark in a carrier color image. Watermark embedding is achieved by modulating the gap of paired coefficient magnitudes in the discrete cosine transform domain according to the intended pixel value, and watermark extraction is the process of regaining and regulating the gap distance back to the intensity value. In a comparison study of robustness against commonly encountered attacks, the proposed scheme outperformed seven watermarking schemes in terms of zero-normalized cross-correlation (ZNCC). To render a better visual rendition of the recovered color watermark, a generative adversarial network (GAN) was introduced to perform image denoising and super-resolution reconstruction. Except for JPEG compression attacks, the proposed scheme generally resulted in ZNCCs higher than 0.65. The employed GAN contributed to a noticeable improvement in perceptual quality, which is also manifested as high-level ZNCCs of no less than 0.78. Full article
(This article belongs to the Special Issue Image Denoising and Image Super-resolution for Sensing Application)
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19 pages, 6725 KiB  
Article
NG-GAN: A Robust Noise-Generation Generative Adversarial Network for Generating Old-Image Noise
by Sadat Hossain and Bumshik Lee
Sensors 2023, 23(1), 251; https://doi.org/10.3390/s23010251 - 26 Dec 2022
Viewed by 3379
Abstract
Numerous old images and videos were captured and stored under unfavorable conditions. Hence, old images and videos have uncertain and different noise patterns compared with those of modern ones. Denoising old images is an effective technique for reconstructing a clean image containing crucial [...] Read more.
Numerous old images and videos were captured and stored under unfavorable conditions. Hence, old images and videos have uncertain and different noise patterns compared with those of modern ones. Denoising old images is an effective technique for reconstructing a clean image containing crucial information. However, obtaining noisy-clean image pairs for denoising old images is difficult and challenging for supervised learning. Preparing such a pair is expensive and burdensome, as existing denoising approaches require a considerable number of noisy-clean image pairs. To address this issue, we propose a robust noise-generation generative adversarial network (NG-GAN) that utilizes unpaired datasets to replicate the noise distribution of degraded old images inspired by the CycleGAN model. In our proposed method, the perception-based image quality evaluator metric is used to control noise generation effectively. An unpaired dataset is generated by selecting clean images with features that match the old images to train the proposed model. Experimental results demonstrate that the dataset generated by our proposed NG-GAN can better train state-of-the-art denoising models by effectively denoising old videos. The denoising models exhibit significantly improved peak signal-to-noise ratios and structural similarity index measures of 0.37 dB and 0.06 on average, respectively, on the dataset generated by our proposed NG-GAN. Full article
(This article belongs to the Special Issue Image Denoising and Image Super-resolution for Sensing Application)
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17 pages, 62326 KiB  
Article
Denoising Single Images by Feature Ensemble Revisited
by Masud An Nur Islam Fahim, Nazmus Saqib, Shafkat Khan Siam and Ho Yub Jung
Sensors 2022, 22(18), 7080; https://doi.org/10.3390/s22187080 - 19 Sep 2022
Cited by 2 | Viewed by 1668
Abstract
Image denoising is still a challenging issue in many computer vision subdomains. Recent studies have shown that significant improvements are possible in a supervised setting. However, a few challenges, such as spatial fidelity and cartoon-like smoothing, remain unresolved or decisively overlooked. Our study [...] Read more.
Image denoising is still a challenging issue in many computer vision subdomains. Recent studies have shown that significant improvements are possible in a supervised setting. However, a few challenges, such as spatial fidelity and cartoon-like smoothing, remain unresolved or decisively overlooked. Our study proposes a simple yet efficient architecture for the denoising problem that addresses the aforementioned issues. The proposed architecture revisits the concept of modular concatenation instead of long and deeper cascaded connections, to recover a cleaner approximation of the given image. We find that different modules can capture versatile representations, and a concatenated representation creates a richer subspace for low-level image restoration. The proposed architecture’s number of parameters remains smaller than in most of the previous networks and still achieves significant improvements over the current state-of-the-art networks. Full article
(This article belongs to the Special Issue Image Denoising and Image Super-resolution for Sensing Application)
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