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Recent Advances in Image Processing

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 May 2024) | Viewed by 28691

Special Issue Editors

School of Computer Science, Wuhan University, Wuhan 430072, China
Interests: image enhancement; person re-identification; video super-resolution
Special Issues, Collections and Topics in MDPI journals
Hubei Key Laboratory of Transportation Internet of Things, School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan,430070, China
Interests: artificial intelligence, computer vision, multimedia information processing, neuromorphic computing
Special Issues, Collections and Topics in MDPI journals
School of Computer Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
Interests: image restoration; image/video quality assessment; video compression

Special Issue Information

Dear Colleagues,

Image processing is the term for performing operations on images, for example, with the aim of enhancing the image, obtaining information from the image, etc. With the great efforts of researchers over the course of decades, algorithms and systems have been developed to make computers as intelligent as humans, by mimicking the human vision system. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues remain open, resulting in the need for the development of novel approaches. The aim of this Special Issue on “Advances in Image Processing” is to give researchers the opportunity to provide new trends, the latest achievements and research directions, and to present their current work on important problems in image processing.

Potential topics of interest for this Special Issue include (but are not limited to) the following:

  • Image quality assessment;
  • Image restoration and enhancement;
  • Image semantic segmentation;
  • Biomedical image processing;
  • Aerial image processing;
  • Multispectral image processing;
  • Hardware and architecture for image processing;
  • Image-processing-oriented datasets;
  • Image retrieval and indexing;
  • Image compression;
  • Low-level and high-level image representation;
  • Mathematical methods in image processing, analysis and representation;
  • Deep learning tools in image processing;
  • Digital watermarking;
  • Digital photography.

Dr. Zheng Wang
Dr. Xian Zhong
Dr. Liang Liao
Dr. Kui Jiang
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 processing
  • image enhancement
  • deep learning
  • image understanding

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Published Papers (12 papers)

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Research

20 pages, 17077 KiB  
Article
Joint Luminance Adjustment and Color Correction for Low-Light Image Enhancement Network
by Nenghuan Zhang, Xiao Han, Chenming Liu, Ruipeng Gang, Sai Ma and Yizhen Cao
Appl. Sci. 2024, 14(14), 6320; https://doi.org/10.3390/app14146320 - 19 Jul 2024
Viewed by 1075
Abstract
Most of the existing low-light enhancement research focuses on global illumination enhancement while ignoring the issues of brightness unevenness and color distortion. To address this dilemma, we propose a low-light image enhancement method that can achieve good performance in luminance adjustment and color [...] Read more.
Most of the existing low-light enhancement research focuses on global illumination enhancement while ignoring the issues of brightness unevenness and color distortion. To address this dilemma, we propose a low-light image enhancement method that can achieve good performance in luminance adjustment and color correction simultaneously. Specifically, the Luminance Adjustment Module is designed to model the global luminance adjustment parameters while taking into account the relationship between global and local illumination features, in order to prevent overexposure or underexposure. Furthermore, we design a Color Correction Module based on the attention mechanism, which utilizes the attention mechanism to capture global color features and correct the color deviation in the illumination-enhanced image. Additionally, we design a color loss function based on a 14-dimensional statistical feature vector related to color, enabling further restoration of the image’s true color. We conduct empirical studies on multiple public low-light datasets, demonstrating that the proposed method outperforms other representative state-of-the-art models regarding illumination enhancement and color correction. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing)
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20 pages, 38504 KiB  
Article
Enhancing X-ray Security Image Synthesis: Advanced Generative Models and Innovative Data Augmentation Techniques
by Bilel Yagoub, Mahmoud SalahEldin Kasem and Hyun-Soo Kang
Appl. Sci. 2024, 14(10), 3961; https://doi.org/10.3390/app14103961 - 7 May 2024
Cited by 1 | Viewed by 1069
Abstract
This study addresses the field of X-ray security screening and focuses on synthesising realistic X-ray images using advanced generative models. Insufficient training data in this area pose a major challenge, which we address through innovative data augmentation techniques. We utilise the power of [...] Read more.
This study addresses the field of X-ray security screening and focuses on synthesising realistic X-ray images using advanced generative models. Insufficient training data in this area pose a major challenge, which we address through innovative data augmentation techniques. We utilise the power of generative adversarial networks (GANs) and conditional GANs (cGANs), in particular the Pix2Pix and Pix2PixHD models, to investigate the generation of X-ray images from various inputs such as masks and edges. Our experiments conducted on a Korean dataset containing dangerous objects relevant to security screening show the effectiveness of these models in improving the quality and realism of image synthesis. Quantitative evaluations based on metrics such as PSNR, SSIM, LPIPS, FID, and FSIM, with scores of 19.93, 0.71, 0.12, 29.36, and 0.54, respectively, show the superiority of our strategy, especially when integrated with hybrid inputs containing both edges and masks. Overall, our results highlight the potential of advanced generative models to overcome the challenges of data scarcity in X-ray security screening and pave the way for more efficient and accurate inspection systems. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing)
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22 pages, 8429 KiB  
Article
Infrared Ship Target Detection Based on Dual Channel Segmentation Combined with Multiple Features
by Dongming Lu, Jiangyun Tan, Mengke Wang, Longyin Teng, Liping Wang and Guohua Gu
Appl. Sci. 2023, 13(22), 12247; https://doi.org/10.3390/app132212247 - 12 Nov 2023
Cited by 2 | Viewed by 1053
Abstract
In infrared images of the sea surface, apart from the complex background of the sea surface, there are often sky and island backgrounds. The disturbances caused by sea wind and the reflection of intense sunlight on the sea surface increase the complexity of [...] Read more.
In infrared images of the sea surface, apart from the complex background of the sea surface, there are often sky and island backgrounds. The disturbances caused by sea wind and the reflection of intense sunlight on the sea surface increase the complexity of the background, which seriously hinders the detection of targets. To achieve the detection of dark-polarity ship targets in such environments, a dual-channel threshold segmentation method based on local low-gray region detection and geometric features judgment is proposed in this paper. In one channel, adaptive threshold segmentation is performed on the low-gray regions of the acquired image and combined with geometric features to obtain a finer segmentation result. In the other channel, adaptive segmentation is performed on the preprocessed image, and potential backgrounds that may be finely segmented as targets are filtered out based on an area threshold. Finally, the results of the two channels are multiplied and fused to obtain an accurate segmentation result. Experimental results demonstrate that the proposed algorithm outperforms the comparison algorithm in subjective and objective evaluations. The proposed algorithm in this paper not only achieves a low false alarm rate but also exhibits a higher detection rate, and the average detection rate in the test sequence surpasses 95%. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing)
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13 pages, 4716 KiB  
Article
Low-Light Image Enhancement Algorithm Based on Deep Learning and Retinex Theory
by Chenyu Lei and Qichuan Tian
Appl. Sci. 2023, 13(18), 10336; https://doi.org/10.3390/app131810336 - 15 Sep 2023
Cited by 2 | Viewed by 4204
Abstract
To address the challenges of low-light images, such as low brightness, poor contrast, and high noise, a network model based on deep learning and Retinex theory is proposed. The model consists of three modules: image decomposition, illumination enhancement, and color restoration. In the [...] Read more.
To address the challenges of low-light images, such as low brightness, poor contrast, and high noise, a network model based on deep learning and Retinex theory is proposed. The model consists of three modules: image decomposition, illumination enhancement, and color restoration. In the image decomposition module, dilated convolutions and residual connections are employed to mitigate the issue of detail loss during the decomposition process. The illumination enhancement module utilizes a set of mapping curves to enhance the illumination map. The color restoration module employs a weighted fusion of a 3D lookup table (3DLUT) to mitigate color distortion in the images. The experimental results demonstrate that the proposed algorithm effectively improves the brightness and contrast of low-light images while addressing the issues of detail loss and color distortion. Compared to other algorithms, it achieves better subjective and objective evaluations. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing)
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14 pages, 1775 KiB  
Article
Action Recognition via Adaptive Semi-Supervised Feature Analysis
by Zengmin Xu, Xiangli Li, Jiaofen Li, Huafeng Chen and Ruimin Hu
Appl. Sci. 2023, 13(13), 7684; https://doi.org/10.3390/app13137684 - 29 Jun 2023
Viewed by 1006
Abstract
This study presents a new semi-supervised action recognition method via adaptive feature analysis. We assume that action videos can be regarded as data points in embedding manifold subspace, and their matching problem can be quantified through a specific Grassmannian kernel function while integrating [...] Read more.
This study presents a new semi-supervised action recognition method via adaptive feature analysis. We assume that action videos can be regarded as data points in embedding manifold subspace, and their matching problem can be quantified through a specific Grassmannian kernel function while integrating feature correlation exploration and data similarity measurement into a joint framework. By maximizing the intra-class compactness based on labeled data, our algorithm can learn multiple features and leverage unlabeled data to enhance recognition. We introduce the Grassmannian kernels and the Projected Barzilai–Borwein (PBB) method to train a subspace projection matrix as a classifier. Experiment results show our method has outperformed the compared approaches when a few labeled training samples are available. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing)
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21 pages, 7625 KiB  
Article
Super-Resolution Reconstruction of Depth Image Based on Kriging Interpolation
by Tingsheng Huang, Xinjian Wang, Chunyang Wang, Xuelian Liu and Yanqing Yu
Appl. Sci. 2023, 13(6), 3769; https://doi.org/10.3390/app13063769 - 15 Mar 2023
Cited by 6 | Viewed by 2310
Abstract
The super-resolution of depth images is a research hotspot. In this study, the classical Kriging algorithm is applied to the spatial interpolation of depth images, together with the fractional-order differential method for edge recognition, to realise the super-resolution reconstruction of depth images. The [...] Read more.
The super-resolution of depth images is a research hotspot. In this study, the classical Kriging algorithm is applied to the spatial interpolation of depth images, together with the fractional-order differential method for edge recognition, to realise the super-resolution reconstruction of depth images. The resulting interpolation model improves the edge performance of Kriging interpolation by harnessing the superior characteristics of fractional-order differential edge recognition and effectively solving the edge blurring problem in super-resolution interpolation of depth images. Experimental results show that, compared with the classical algorithms, the super-resolution reconstruction based on Kriging interpolation is greatly improved in terms of visual effects and the peak signal-to-noise ratio of the depth image. In particular, edge recognition based on fractional-order differentiation solves the image blurring problem at the edges of the depth images. Inspection of the point clouds of the depth images shows that the output of the proposed interpolation model has obvious fractal characteristics. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing)
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16 pages, 10990 KiB  
Communication
Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
by Erkang Chen, Tian Ye, Jingxia Jiang, Lihan Tong and Qiubo Ye
Appl. Sci. 2023, 13(6), 3739; https://doi.org/10.3390/app13063739 - 15 Mar 2023
Cited by 2 | Viewed by 1545
Abstract
Real-world nonhomogeneous haze brings challenges to image restoration. More efforts are needed to remove dense haze and thin haze simultaneously and efficiently. However, most existing dehazing methods do not pay attention to the complex distributions of haze and usually suffer from a low [...] Read more.
Real-world nonhomogeneous haze brings challenges to image restoration. More efforts are needed to remove dense haze and thin haze simultaneously and efficiently. However, most existing dehazing methods do not pay attention to the complex distributions of haze and usually suffer from a low runtime speed. To tackle such problems, we present an efficient re-parameterization residual attention network (RRA-Net), whose design has three key aspects. Firstly, we propose a training-time multi-branch residual attention block (MRAB), where multi-scale convolutions in different branches cope with the nonuniformity of haze and are converted into a single-path convolution during inference. It also features local residual learning with improved spatial attention and channel attention, allowing dense and thin haze to be attended to differently. Secondly, our lightweight network structure cascades six MRABs followed by a long skip connection with attention and a fusion tail. Overall, our RRA-Net only has about 0.3M parameters. Thirdly, two new loss functions, namely the Laplace pyramid loss and the color attenuation loss, help train the network to recover details and colors. The experimental results show that the proposed RRA-Net performs favorably against state-of-the-art dehazing methods on real-world image datasets, including both nonhomogeneous haze and dense homogeneous haze. A runtime comparison under the same hardware setup also demonstrates the superior efficiency of the proposed network. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing)
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23 pages, 7972 KiB  
Article
Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
by Shijun Chen, Jing Huang, Hengfeng Miao, Yaoqing Cai, Yuanqiao Wen and Changshi Xiao
Appl. Sci. 2023, 13(5), 3164; https://doi.org/10.3390/app13053164 - 1 Mar 2023
Cited by 1 | Viewed by 1712
Abstract
Waterline usually plays as an important visual cue for the autonomous navigation of marine unmanned surface vehicles (USVs) in specific waters. However, the visual complexity of the inland waterline presents a significant challenge for the development of highly efficient computer vision algorithms tailored [...] Read more.
Waterline usually plays as an important visual cue for the autonomous navigation of marine unmanned surface vehicles (USVs) in specific waters. However, the visual complexity of the inland waterline presents a significant challenge for the development of highly efficient computer vision algorithms tailored for waterline detection in a complicated inland water environment that marine USVs face. This paper attempts to find a solution to guarantee the effectiveness of waterline detection for the USVs with a general digital camera patrolling variable inland waters. To this end, a general deep-learning-based paradigm for inland marine USVs, named DeepWL, is proposed, which consists of two cooperative deep models (termed WLdetectNet and WLgenerateNet, respectively). They afford a continuous waterline image-map estimation from a single video stream captured on board. Experimental results demonstrate the effectiveness and superiority of the proposed approach via qualitative and quantitative assessment on the concerned performances. Moreover, due to its own generality, the proposed approach has the potential to be applied to the waterline detection tasks of other water areas such as coastal waters. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing)
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13 pages, 797 KiB  
Article
A Lightweight YOLOv5 Optimization of Coordinate Attention
by Jun Wu, Jiaming Dong, Wanyu Nie and Zhiwei Ye
Appl. Sci. 2023, 13(3), 1746; https://doi.org/10.3390/app13031746 - 30 Jan 2023
Cited by 10 | Viewed by 4718
Abstract
As Machine Learning technologies evolve, there is a desire to add vision capabilities to all devices within the IoT in order to enable a wider range of artificial intelligence. However, for most mobile devices, their computing power and storage space are affected by [...] Read more.
As Machine Learning technologies evolve, there is a desire to add vision capabilities to all devices within the IoT in order to enable a wider range of artificial intelligence. However, for most mobile devices, their computing power and storage space are affected by factors such as cost and the tight supply of relevant chips, making it impossible to effectively deploy complex network models to small processors with limited resources and to perform efficient real-time detection. In this paper, YOLOv5 is studied to achieve the goal of lightweight devices by reducing the number of original network channels. Then detection accuracy is guaranteed by adding a detection head and CA attention mechanism. The YOLOv5-RC model proposed in this paper is 30% smaller and lighter than YOLOv5s, but still maintains good detection accuracy. YOLOv5-RC network models can achieve a good balance between detection accuracy and detection speed, with potential for its widespread use in industry. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing)
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16 pages, 5299 KiB  
Article
Normalized Weighting Schemes for Image Interpolation Algorithms
by Olivier Rukundo
Appl. Sci. 2023, 13(3), 1741; https://doi.org/10.3390/app13031741 - 29 Jan 2023
Cited by 4 | Viewed by 2332
Abstract
Image interpolation algorithms pervade many modern image processing and analysis applications. However, when their weighting schemes inefficiently generate very unrealistic estimates, they may negatively affect the performance of the end-user applications. Therefore, in this work, the author introduced four weighting schemes based on [...] Read more.
Image interpolation algorithms pervade many modern image processing and analysis applications. However, when their weighting schemes inefficiently generate very unrealistic estimates, they may negatively affect the performance of the end-user applications. Therefore, in this work, the author introduced four weighting schemes based on some geometric shapes for digital image interpolation operations. Moreover, the quantity used to express the extent of each shape’s weight was the normalized area, especially when the sums of areas exceeded a unit square size. The introduced four weighting schemes are based on the minimum side-based diameter (MD) of a regular tetragon, hypotenuse-based radius (HR), the virtual pixel length-based height for the area of the triangle (AT), and the virtual pixel length for hypotenuse-based radius for the area of the circle (AC). At the smaller scaling ratio, the image interpolation algorithm based on the HR scheme scored the highest at 66.6% among non-traditional image interpolation algorithms presented. However, at the higher scaling ratio, the AC scheme-based image interpolation algorithm scored the highest at 66.6% among non-traditional algorithms presented, and, here, its image interpolation quality was generally superior or comparable to the quality of images interpolated by both non-traditional and traditional algorithms. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing)
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22 pages, 10086 KiB  
Article
Automatic Measurement of Inclination Angle of Utility Poles Using 2D Image and 3D Point Cloud
by Lei Chen, Jiazhen Chang, Jinli Xu and Zuowei Yang
Appl. Sci. 2023, 13(3), 1688; https://doi.org/10.3390/app13031688 - 28 Jan 2023
Cited by 5 | Viewed by 2721
Abstract
The utility pole inclination angle is an important parameter for determining pole health conditions. Without depth information, the angle cannot be estimated from a 2D image, and without large labeled reference pole data, it is time consuming to locate the pole in the [...] Read more.
The utility pole inclination angle is an important parameter for determining pole health conditions. Without depth information, the angle cannot be estimated from a 2D image, and without large labeled reference pole data, it is time consuming to locate the pole in the 3D point cloud. Therefore, this paper proposes a method that processes the pole data from the 2D image and 3D point cloud to automatically measure the pole inclination angle. Firstly, the mask of the pole skeleton is obtained from an improved Mask R-CNN. Secondly, the pole point cloud is extracted from a PointNet that deals with the generated frustum from the pole skeleton mask and depth map fusion. Finally, the angle is calculated by fitting the central axis of the pole cloud data. ApolloSpace open dataset and laboratory data are used for evaluation. The experimental results show that the AP75 of improved Mask R-CNN is 58.15%, the accuracy of PointNet is 92.4%, the average error of pole inclination is 0.66°, and the variance is 0.12°. It is proved that the method can effectively realize the automatic measurement of pole inclination. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing)
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14 pages, 8261 KiB  
Article
Panchromatic and Multispectral Image Fusion Combining GIHS, NSST, and PCA
by Lina Xu, Guangqi Xie and Sitong Zhou
Appl. Sci. 2023, 13(3), 1412; https://doi.org/10.3390/app13031412 - 20 Jan 2023
Cited by 3 | Viewed by 1904
Abstract
Spatial and spectral information are essential sources of information in remote sensing applications, and the fusion of panchromatic and multispectral images effectively combines the advantages of both. Due to the existence of two main classes of fusion methods—component substitution (CS) and multi-resolution analysis [...] Read more.
Spatial and spectral information are essential sources of information in remote sensing applications, and the fusion of panchromatic and multispectral images effectively combines the advantages of both. Due to the existence of two main classes of fusion methods—component substitution (CS) and multi-resolution analysis (MRA), which have different advantages—mixed approaches are possible. This paper proposes a fusion algorithm that combines the advantages of generalized intensity–hue–saturation (GIHS) and non-subsampled shearlet transform (NSST) with principal component analysis (PCA) technology to extract more spatial information. Therefore, compared with the traditional algorithms, the algorithm in this paper uses PCA transformation to obtain spatial structure components from PAN and MS, which can effectively inject spatial information while maintaining spectral information with high fidelity. First, PCA is applied to each band of low-resolution multispectral (MS) images and panchromatic (PAN) images to obtain the first principal component and to calculate the intensity of MS. Then, the PAN image is fused with the first principal component using NSST, and the fused image is used to replace the original intensity component. Finally, a fused image is obtained using the GIHS algorithm. Using the urban, plants and water, farmland, and desert images from GeoEye-1, WorldView-4, GaoFen-7 (GF-7), and Gaofen Multi-Mode (GFDM) as experimental data, this fusion method was tested using the evaluation mode with references and the evaluation mode without references and was compared with five other classic fusion algorithms. The results showed that the algorithms in this paper had better fusion performances in both spectral preservation and spatial information incorporation. Full article
(This article belongs to the Special Issue Recent Advances in Image Processing)
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