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Information
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Intelligent Image Processing by Deep Learning
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Intelligent Image Processing by Deep Learning

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (27 September 2024) | Viewed by 5534

Special Issue Editors

Prof. Dr. Haiyan Li

E-Mail Website
Guest Editor
School of Information Science & Engineering, Yunnan University, Kunming 650000, China
Interests: artificial intelligence; pattern recognition; image processing
Prof. Dr. Dongming Zhou

E-Mail Website
Guest Editor
School of Information Science & Engineering, Yunnan University, Kunming 650000, China
Interests: artificial intelligence; pattern recognition; image processing

Special Issue Information

Dear Colleagues,

Image processing plays a crucial role in various domains, including computer vision, information analysis, and multimedia applications. With the advent of deep learning and artificial intelligence techniques, there is a growing interest in developing intelligent image processing systems that can effectively analyze, enhance, and interpret images. However, challenges like Dataset Limitations, Interpretability and Explainability, Computational Complexity and many other issues still exit that need to be addressed for further progress and widespread adoption. This Special Issue aims to provide a platform for researchers and practitioners to explore the advancements and applications of deep learning-based approaches in the field of image processing. Authors are encouraged to present novel deep learning-based methodologies, algorithms, and frameworks that contribute to the advancement of intelligent image processing. Submissions may include theoretical contributions, experimental evaluations, and practical applications. We welcome original research papers, survey articles, and systematic literature reviews that address the challenges and opportunities in the field of intelligent image processing using deep learning. Manuscripts should demonstrate the effectiveness, efficiency, and applicability of the proposed methods. We invite original research articles, case studies, and reviews that address the related topics such as:

  1. Image inpainting and restoration using deep learning techniques;
  2. Advanced image analysis and understanding;
  3. Information processing and analysis in images;
  4. Computer vision algorithms and applications;
  5. Machine learning for image processing and analysis;
  6. Intelligent image processing system design and implementation;
  7. Multimodal target monitoring and tracking techniques;
  8. Multimodal image fusion and enhancement approaches;
  9. Application and case studies of deep learning in image processing.

Prof. Dr. Haiyan Li
Prof. Dr. Dongming Zhou
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. Information is an international peer-reviewed open access monthly 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 1600 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 inpainting
  • image analysis
  • information processing
  • information analysis
  • computer vision
  • deep learning
  • machine learning
  • application and case studies
  • intelligent image processing system
  • multimodal target monitoring and tracking
  • multimodal image fusion
  • artificial intelligence

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Published Papers (4 papers)

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Research

29 pages, 5728 KiB  
Article
Hierarchical Graph Neural Network: A Lightweight Image Matching Model with Enhanced Message Passing of Local and Global Information in Hierarchical Graph Neural Networks
by Enoch Opanin Gyamfi, Zhiguang Qin, Juliana Mantebea Danso and Daniel Adu-Gyamfi
Information 2024, 15(10), 602; https://doi.org/10.3390/info15100602 - 30 Sep 2024
Viewed by 786
Abstract
Graph Neural Networks (GNNs) have gained popularity in image matching methods, proving useful for various computer vision tasks like Structure from Motion (SfM) and 3D reconstruction. A well-known example is SuperGlue. Lightweight variants, such as LightGlue, have been developed with a focus on [...] Read more.
Graph Neural Networks (GNNs) have gained popularity in image matching methods, proving useful for various computer vision tasks like Structure from Motion (SfM) and 3D reconstruction. A well-known example is SuperGlue. Lightweight variants, such as LightGlue, have been developed with a focus on stacking fewer GNN layers compared to SuperGlue. This paper proposes the h-GNN, a lightweight image matching model, with improvements in the two processing modules, the GNN and matching modules. After image features are detected and described as keypoint nodes of a base graph, the GNN module, which primarily aims at increasing the h-GNN’s depth, creates successive hierarchies of compressed-size graphs from the base graph through a clustering technique termed SC+PCA. SC+PCA combines Principal Component Analysis (PCA) with Spectral Clustering (SC) to enrich nodes with local and global information during graph clustering. A dual non-contrastive clustering loss is used to optimize graph clustering. Additionally, four message-passing mechanisms have been proposed to only update node representations within a graph cluster at the same hierarchical level or to update node representations across graph clusters at different hierarchical levels. The matching module performs iterative pairwise matching on the enriched node representations to obtain a scoring matrix. This matrix comprises scores indicating potential correct matches between the image keypoint nodes. The score matrix is refined with a ‘dustbin’ to further suppress unmatched features. There is a reprojection loss used to optimize keypoint match positions. The Sinkhorn algorithm generates a final partial assignment from the refined score matrix. Experimental results demonstrate the performance of the proposed h-GNN against competing state-of-the-art (SOTA) GNN-based methods on several image matching tasks under homography, estimation, indoor and outdoor camera pose estimation, and 3D reconstruction on multiple datasets. Experiments also demonstrate improved computational memory and runtime, approximately 38.1% and 26.14% lower than SuperGlue, and an average of about 6.8% and 7.1% lower than LightGlue. Future research will explore the effects of integrating more recent simplicial message-passing mechanisms, which concurrently update both node and edge representations, into our proposed model. Full article
(This article belongs to the Special Issue Intelligent Image Processing by Deep Learning)
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14 pages, 1516 KiB  
Article
Early Recurrence Prediction of Hepatocellular Carcinoma Using Deep Learning Frameworks with Multi-Task Pre-Training
by Jian Song, Haohua Dong, Youwen Chen, Xianru Zhang, Gan Zhan, Rahul Kumar Jain and Yen-Wei Chen
Information 2024, 15(8), 493; https://doi.org/10.3390/info15080493 - 17 Aug 2024
Cited by 1 | Viewed by 892
Abstract
Post-operative early recurrence (ER) of hepatocellular carcinoma (HCC) is a major cause of mortality. Predicting ER before treatment can guide treatment and follow-up protocols. Deep learning frameworks, known for their superior performance, are widely used in medical imaging. However, they face challenges due [...] Read more.
Post-operative early recurrence (ER) of hepatocellular carcinoma (HCC) is a major cause of mortality. Predicting ER before treatment can guide treatment and follow-up protocols. Deep learning frameworks, known for their superior performance, are widely used in medical imaging. However, they face challenges due to limited annotated data. We propose a multi-task pre-training method using self-supervised learning with medical images for predicting the ER of HCC. This method involves two pretext tasks: phase shuffle, focusing on intra-image feature representation, and case discrimination, focusing on inter-image feature representation. The effectiveness and generalization of the proposed method are validated through two different experiments. In addition to predicting early recurrence, we also apply the proposed method to the classification of focal liver lesions. Both experiments show that the multi-task pre-training model outperforms existing pre-training (transfer learning) methods with natural images, single-task self-supervised pre-training, and DINOv2. Full article
(This article belongs to the Special Issue Intelligent Image Processing by Deep Learning)
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16 pages, 2278 KiB  
Article
Deep Learning to Authenticate Traditional Handloom Textile
by Anindita Das, Aniruddha Deka, Kishore Medhi and Manob Jyoti Saikia
Information 2024, 15(8), 465; https://doi.org/10.3390/info15080465 - 4 Aug 2024
Viewed by 1374
Abstract
Handloom textile products play an essential role in both the financial and cultural landscape of natives, necessitating accurate and efficient methods for authenticating against replicated powerloom textiles for the protection of heritage and indigenous weavers’ economic viability. This paper presents a new approach [...] Read more.
Handloom textile products play an essential role in both the financial and cultural landscape of natives, necessitating accurate and efficient methods for authenticating against replicated powerloom textiles for the protection of heritage and indigenous weavers’ economic viability. This paper presents a new approach to the automated identification of handloom textiles leveraging a deep metric learning technique. A labeled handloom textile dataset of 25,166 images was created by collecting handloom textile samples of six unique types, working with indigenous weavers in Assam, Northeast India. The proposed method achieved remarkable success by acquiring biased feature representations that facilitate the effective separation of different fiber types in a learned feature space. Through extensive experimentation and comparison with baseline models, our approach demonstrated superior efficiency in classifying handloom textiles with an accuracy of 97.8%. Our approach not only contributes to the preservation and promotion of traditional textile craftsmanship in the region but also highlights its significance. Full article
(This article belongs to the Special Issue Intelligent Image Processing by Deep Learning)
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15 pages, 6946 KiB  
Article
MCF-YOLOv5: A Small Target Detection Algorithm Based on Multi-Scale Feature Fusion Improved YOLOv5
by Song Gao, Mingwang Gao and Zhihui Wei
Information 2024, 15(5), 285; https://doi.org/10.3390/info15050285 - 17 May 2024
Viewed by 1841
Abstract
In recent years, many deep learning-based object detection methods have performed well in various applications, especially in large-scale object detection. However, when detecting small targets, previous object detection algorithms cannot achieve good results due to the characteristics of the small targets themselves. To [...] Read more.
In recent years, many deep learning-based object detection methods have performed well in various applications, especially in large-scale object detection. However, when detecting small targets, previous object detection algorithms cannot achieve good results due to the characteristics of the small targets themselves. To address the aforementioned issues, we propose the small object algorithm model MCF-YOLOv5, which has undergone three improvements based on YOLOv5. Firstly, a data augmentation strategy combining Mixup and Mosaic is used to increase the number of small targets in the image and reduce the interference of noise and changes in detection. Secondly, in order to accurately locate the position of small targets and reduce the impact of unimportant information on small targets in the image, the attention mechanism coordinate attention is introduced in YOLOv5’s neck network. Finally, we improve the Feature Pyramid Network (FPN) structure and add a small object detection layer to enhance the feature extraction ability of small objects and improve the detection accuracy of small objects. The experimental results show that, with a small increase in computational complexity, the proposed MCF-YOLOv5 achieves better performance than the baseline on both the VisDrone2021 dataset and the Tsinghua Tencent100K dataset. Compared with YOLOv5, MCF-YOLOv5 has improved detection APsmall by 3.3% and 3.6%, respectively. Full article
(This article belongs to the Special Issue Intelligent Image Processing by Deep Learning)
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