Intelligent Image Processing Technology

Topic Information

Dear Colleagues,

Rapid advancements in intelligent image processing technology have revolutionized various fields by enabling more accurate, efficient, and innovative approaches to image analysis and interpretation. This Topic seeks to explore the multifaceted applications and theoretical developments driving this transformative discipline forward.

Intelligent image processing integrates sophisticated algorithms, machine learning techniques, and computational methodologies to extract meaningful information from digital images. It plays a crucial role in various domains such as medical imaging, remote sensing, industrial automation, and beyond.

Contributions to this Topic will encompass the following topics:

• Algorithmic Innovations: novel algorithms for image enhancement, segmentation, object detection, and pattern recognition.
• Applications in Remote Sensing: the utilization of intelligent image processing for analyzing satellite imagery and aerial photography.
• Medical Image Analysis: advances in diagnostic imaging, image registration, and computer-aided diagnosis.
• Integration with Sensor Networks: enhancing sensor data interpretation and analysis.
• Industrial Automation: applications in robotics, quality control, and manufacturing processes.
• Machine Learning and Reinforcement Learning: the optimization of image processing tasks and system performance.
• Multimodal Information Processing: integration with natural language processing, speech recognition, and data mining.
• Autonomous Agents: enhancing decision-making and control capabilities.

Researchers are invited to contribute original research articles, reviews, and methodological studies that explore the frontiers of intelligent image processing across these different areas. This Topic aims to foster interdisciplinary collaboration, showcase cutting-edge developments, and outline future directions in this dynamic field.

By providing a comprehensive platform for researchers and practitioners, this Topic aims to advance the understanding and application of intelligent image processing technology, paving the way for new discoveries and innovations across various disciplines.

Dr. Lei Shi
Prof. Dr. Didier El Baz
Prof. Dr. Jinping Liu
Dr. Yujin Zhang
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	18.4 Days	CHF 2400	Submit
Journal of Imaging jimaging	2.7	5.9	2015	18.3 Days	CHF 1800	Submit
Mathematics mathematics	2.3	4.0	2013	18.3 Days	CHF 2600	Submit
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Information information	2.4	6.9	2010	16.4 Days	CHF 1600	Submit

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Published Papers (4 papers)

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All Applied Sciences J. Imaging Mathematics Electronics Information

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21 pages, 9110 KiB  

Open AccessArticle

SwinTCS: A Swin Transformer Approach to Compressive Sensing with Non-Local Denoising

by Xiuying Li, Haoze Li, Hongwei Liao, Zhufeng Suo, Xuesong Chen and Jiameng Han

J. Imaging 2025, 11(5), 139; https://doi.org/10.3390/jimaging11050139 - 29 Apr 2025

Viewed by 130

Abstract

In the era of the Internet of Things (IoT), the rapid growth of interconnected devices has intensified the demand for efficient data acquisition and processing techniques. Compressive Sensing (CS) has emerged as a promising approach for simultaneous signal acquisition and dimensionality reduction, particularly [...] Read more.

In the era of the Internet of Things (IoT), the rapid growth of interconnected devices has intensified the demand for efficient data acquisition and processing techniques. Compressive Sensing (CS) has emerged as a promising approach for simultaneous signal acquisition and dimensionality reduction, particularly in multimedia applications. In response to the challenges presented by traditional CS reconstruction methods, such as boundary artifacts and limited robustness, we propose a novel hierarchical deep learning framework, SwinTCS, for CS-aware image reconstruction. Leveraging the Swin Transformer architecture, SwinTCS integrates a hierarchical feature representation strategy to enhance global contextual modeling while maintaining computational efficiency. Moreover, to better capture local features of images, we introduce an auxiliary convolutional neural network (CNN). Additionally, for suppressing noise and improving reconstruction quality in high-compression scenarios, we incorporate a Non-Local Means Denoising module. The experimental results on multiple public benchmark datasets indicate that SwinTCS surpasses State-of-the-Art (SOTA) methods across various evaluation metrics, thereby confirming its superior performance. Full article

(This article belongs to the Topic Intelligent Image Processing Technology)

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20 pages, 8414 KiB  

Open AccessArticle

ADCNet: Anomaly-Driven Cross-Modal Contrastive Network for Medical Report Generation

by Yuxue Liu, Junsan Zhang, Kai Liu and Lizhuang Tan

Electronics 2025, 14(3), 532; https://doi.org/10.3390/electronics14030532 - 28 Jan 2025

Viewed by 786

Abstract

Medical report generation has made significant progress in recent years. However, generated reports still suffer from issues such as poor readability, incomplete and inaccurate descriptions of lesions, and challenges in capturing fine-grained abnormalities. The primary obstacles include low image resolution, poor contrast, and [...] Read more.

Medical report generation has made significant progress in recent years. However, generated reports still suffer from issues such as poor readability, incomplete and inaccurate descriptions of lesions, and challenges in capturing fine-grained abnormalities. The primary obstacles include low image resolution, poor contrast, and substantial cross-modal discrepancies between visual and textual features. To address these challenges, we propose an Anomaly-Driven Cross-Modal Contrastive Network (ADCNet), which aims to enhance the quality and accuracy of medical report generation through effective cross-modal feature fusion and alignment. First, we design an anomaly-aware cross-modal feature fusion (ACFF) module that introduces an anomaly embedding vector to guide the extraction and generation of anomaly-related features from visual representations. This process enhances the capability of visual features to capture lesion-related abnormalities and improves the performance of feature fusion. Second, we propose a fine-grained regional feature alignment (FRFA) module, which dynamically filters visual and textual features to suppress irrelevant information and background noise. This module computes cross-modal relevance to align fine-grained regional features, ensuring improved semantic consistency between images and generated reports. The experimental results from the IU X-Ray and MIMIC-CXR datasets demonstrate that the proposed ADCNet method significantly outperforms existing approaches. Specifically, ADCNet achieves notable improvements in natural language generation metrics, as well as the accuracy, completeness, and fluency of medical report generation. Full article

(This article belongs to the Topic Intelligent Image Processing Technology)

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22 pages, 28158 KiB  

Open AccessArticle

Edge-Aware Dual-Task Image Watermarking Against Social Network Noise

by Hao Jiang, Jiahao Wang, Yuhan Yao, Xingchen Li, Feifei Kou, Xinkun Tang and Limei Qi

Appl. Sci. 2025, 15(1), 57; https://doi.org/10.3390/app15010057 - 25 Dec 2024

Viewed by 840

Abstract

In the era of widespread digital image sharing on social media platforms, deep-learning-based watermarking has shown great potential in copyright protection. To address the fundamental trade-off between the visual quality of the watermarked image and the robustness of watermark extraction, we explore the [...] Read more.

In the era of widespread digital image sharing on social media platforms, deep-learning-based watermarking has shown great potential in copyright protection. To address the fundamental trade-off between the visual quality of the watermarked image and the robustness of watermark extraction, we explore the role of structural features and propose a novel edge-aware watermarking framework. Our primary innovation lies in the edge-aware secret hiding module (EASHM), which achieves adaptive watermark embedding by aligning watermarks with image structural features. To realize this, the EASHM leverages knowledge distillation from an edge detection teacher and employs a dual-task encoder that simultaneously performs edge detection and watermark embedding through maximal parameter sharing. The framework is further equipped with a social network noise simulator (SNNS) and a secret recovery module (SRM) to enhance robustness against common image noise attacks. Extensive experiments on three public datasets demonstrate that our framework achieves superior watermark imperceptibility, with PSNR and SSIM values exceeding 40.82 dB and 0.9867, respectively, while maintaining an over 99% decoding accuracy under various noise attacks, outperforming existing methods by significant margins. Full article

(This article belongs to the Topic Intelligent Image Processing Technology)

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15 pages, 2181 KiB  

Open AccessArticle

Micro-Expression Recognition Algorithm Using Regions of Interest and the Weighted ArcFace Loss

by Peiying Zhang, Ruixin Wang, Jia Luo and Lei Shi

Electronics 2025, 14(1), 2; https://doi.org/10.3390/electronics14010002 - 24 Dec 2024

Viewed by 797

Abstract

Micro-expressions often reveal more genuine emotions but are challenging to recognize due to their brief duration and subtle amplitudes. To address these challenges, this paper introduces a micro-expression recognition method leveraging regions of interest (ROIs). Firstly, four specific ROIs are selected based on [...] Read more.

Micro-expressions often reveal more genuine emotions but are challenging to recognize due to their brief duration and subtle amplitudes. To address these challenges, this paper introduces a micro-expression recognition method leveraging regions of interest (ROIs). Firstly, four specific ROIs are selected based on an analysis of the optical flow and relevant action units activated during micro-expressions. Secondly, effective feature extraction is achieved using the optical flow method. Thirdly, a block partition module is integrated into a convolutional neural network to reduce computational complexity, thereby enhancing model accuracy and generalization. The proposed model achieves notable performance, with accuracies of 93.96%, 86.15%, and 81.17% for three-class recognition on the CASME II, SAMM, and SMIC datasets, respectively. For five-class recognition, the model achieves accuracies of 81.63% on the CASME II dataset and 84.31% on the SMIC dataset. Experimental results validate the effectiveness of using ROIs in improving micro-expression recognition accuracy. Full article

(This article belongs to the Topic Intelligent Image Processing Technology)

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