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Lightweight image Quality Prediction Guided by Perceptual Ranking Feedback

Lightweight Image Quality Prediction Guided by Perceptual Ra...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Aymen Sekhri Mohamed-Chaker Larabi Seyed Ali Amirshahi CNRS Université de Poitiers XLIM Poitiers France Norwegian University of Science and Technology Gjøvik Norway

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Automatic image Quality Assessment (IQA) remains a difficult challenge due to the complexity of mimicking the Human Visual System (HVS) and the limitations of traditional objective image Quality Metrics (IQM). Existing learnable methods often involve high computational costs and fail to adequately capture the nuanced perceptual characteristics of the HVS, including the human ability to rank image quality and human sensitivity to differences in areas with high-frequency. In this study, we propose an effective approach that addresses these challenges by incorporating the characteristics of HVS and the perceptual classification into a lightweight IQM framework based on the transformer architecture. This allows our method to capture long-range dependencies effectively. Our approach leverages Objective Error Maps (OEMs) to enhance sensitivity to visual errors and employs a ranking module as an objective function, providing feedback on the perceptual quality at the feature level. Experimental results demonstrate that our approach not only achieves competitive performance compared to state-of-the-art IQMs but also significantly reduces computational complexity.

关键词： image quality Visualization Sensitivity Computer architecture Visual systems signal processing Transformers Linear programming Computational efficiency Speech processing

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Enhanced Edge Attention-based SRCNN for Retinal image Super-Resolution

Enhanced Edge Attention-Based SRCNN for Retinal Image Super-...

引用

Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Manoj Koshika Prabhakar Kandukuri Shyam Kumar Moppey Department of Artificial Intelligence & Machine Learning Chaitanya Bharathi Institute of Technology Gandipet Hyderabad India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

The early recognition of retinal disorders such as cataracts, glaucoma, and retinal problems is highly dependent on high-resolution retinal imaging, which is a complex issue because of poor image quality and the lack of important features. The proposed Enhanced Edge Attention Super Resolution Convolutional Neural Network (EEASRCNN) is designed to improve the quality of retinal images high precisely. The model preserves important details like blood vessels and the optic disc by using a custom edge loss function, sharpening filters, and combined attention mechanisms. With a high structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) score and an accuracy of 97.16% in preserving structural details, EEASRCNN outperforms traditional super-resolution methods after being trained on a dataset of diverse retinal images. EEASRCNN offers effective image super resolution in real-time medical image diagnosis.

关键词： Training image quality Adaptation models Attention mechanisms PSNR image edge detection Computational modeling Superresolution Retina Convolutional neural networks

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Explaining Representations in Correlation-based Deep Multiview Representation Learning

Explaining Representations in Correlation-based Deep Multivi...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Maurice Kuschel Amr Alkhatib Tanuj Hasija Henrik Boström Signal and System Theory Group Paderborn University Paderborn Germany Division of Software and Computer Systems KTH Royal Institue of Technology Stockholm Sweden

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multiview representation learning techniques based on deep correlation maximization have become increasingly popular for learning meaningful and compact representations from multiview data. Even though their performance is state-of-the-art in many interpretability-critical fields, their black-box behavior poses a problem and restricts their usability. To overcome this restriction, we propose XDCCA (eXplanations for Deep Canonical Correlation Analysis), an explanation strategy using characteristic rules in combination with SHAP that exploits the inherent structure of latent spaces created by correlation maximization techniques. We demonstrate how XDCCA allows for interpreting learned representations and their correlation using real medical time series and synthetic image data.

关键词： Representation learning Correlation Time series analysis Closed box signal processing Knowledge discovery Usability Speech processing Alzheimer's disease Biomedical imaging

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Automatic Tumour Segmentation Network based on PLHN Architecture and Dynamic Contrast-Enhanced Magnetic Resonance Imaging

Automatic Tumour Segmentation Network Based on PLHN Architec...

引用

Integrated Circuits and Communication systems (ICICACS), IEEE international conference on

作者： Yaoxuan Xie Jiajian Li Chenyang Rong Yan Cheng College of Software Taiyuan University of Technology Taiyuan China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Tumor diagnosis often requires hemodynamic information to be judged. DCE-MRI technology can record and compare information well and has become an important tool in the diagnosis process. However, at this stage, the automatic segmentation technology for tumors is still not perfect, which is mainly due to the diversity of tumor morphology and signal intensity, and the insufficient performance of existing deep learning models in dealing with complex structures and image noise. In order to solve these problems, this paper proposes an automatic tumor segmentation method based on pyramid hierarchical network (PLHN) architecture and DCE-MRI. This method enhances the network's ability to recognize tumor boundaries and details through multi-scale feature extraction and hierarchical processing, and shows stronger robustness when dealing with dynamic changes in DCE-MRI. According to our experimental results on the data set, the method proposed in this paper can effectively improve the accuracy of tumor segmentation, which has strong research and practical significance.

关键词： image segmentation Accuracy Noise Morphology Medical treatment Feature extraction Robustness Integrated circuit modeling Optimization Tumors

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Smart Wearable Socks Enabled with IoT for Fall Detection

Smart Wearable Socks Enabled with IoT for Fall Detection

引用

Electronics and Renewable systems (ICEARS), international conference on

作者： J. Abanah Shirley R. Jansi Rishabh Vyas Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This paper presents the design and development of a fall detection system that integrates IoT technology to improve the safety and health of the elderly and people with mobility issues. The system employs a piezoresistive Velostat made textile pressure sensor that is encased in two conductive fabric layers. Wearable socks with this sensor built in provide a flexible and discrete way to monitor continuously. The sensor’s piezoresistive nature enables it to record data in real time by detecting changes in pressure when a fall happens. The ESP8266 Wi-Fi module, integrated with the NodeMCU platform, processes real-time pressure data and employs a threshold-based algorithm with signal smoothing and timing conditions to accurately distinguish falls from normal activities. When a fall is detected, an HTTP request is sent via Webhook API to the IFTTT platform, instantly notifying caregivers through email or cloud-based alerts. This system is a cost-effective, non-intrusive solution with potential applications in home care and healthcare settings. It combines textile-based sensing with IoT capabilities to provide continuous monitoring and timely interventions, thereby improving the quality of life for those at risk.

关键词： Pressure sensors Renewable energy sources Medical services Real-time systems Safety internet of Things Fall detection Biomedical monitoring Piezoresistance Monitoring

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Dual Path Unsupervised Real image Denoising

Dual Path Unsupervised Real Image Denoising

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Yao Li Zheng-jun Zha College of Cyber Security University of Science and Technology of China Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Because of the significant gap between the real noise and the synthetic noise domains, the learning-based method trained on the synthetic noise performs poorly on the task of removing the real noise. To solve the problem, the dual path unsupervised real image denoising method is proposed, by using the unsupervised path of the image translation between the real noise domain and the synthetic noise domain and the indirect supervised path of the image denoising from the realistic noisy images to the noisy-free images. The image translation between the real noise domain and the synthetic noise domain augments the training images, and the effective supervised denoising model is used to tackle with the unsupervised real image denoising problem. This method network structure is flexible to adjust the different tasks in the practical applications. Compared to other unsupervised real image denoising methods, our method can adaptively generate the synthetic noise that shares a similar distribution as the real noise. Denoising experiments on real-world sRGB images show the effectiveness of the proposed method.

关键词： Training Learning systems Bridges Translation Noise Noise reduction Collaboration Noise measurement Speech processing image denoising

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Music Classification Model based on Multi-Feature image Fusion and Machine Learning Algorithms

Music Classification Model based on Multi-Feature Image Fusi...

引用

Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Chen Wang School of Children’s Welfare Changchun Humanities and Sciences College Changchun China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

How to effectively classify massive music data has become a key research topic in the field of music information retrieval. This study proposes a novel music classification model that combines multi-feature image fusion (MFIF) and machine learning (ML) algorithms to improve the accuracy and efficiency of music classification. The specific method is to first convert music signals into various visual representations such as spectrograms, Mel spectrograms, and chromaticity maps, and then combine these different visual features into a comprehensive feature map through image fusion technology. In addition, the study also introduced a deep learning framework, using Convolutional Neural Network (CNN) to learn these fused image features. In the absence of noise, the model exhibits high performance with an accuracy of 0.87 and an F1 score of 0.855, indicating that the model can perform music classification tasks well under ideal input conditions. This article provides technical support for related music recommendation systems and music copyright management.

关键词： Deep learning Visualization Accuracy Machine learning algorithms image recognition Computational modeling Multiple signal classification Convolutional neural networks image fusion Spectrogram

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FKAN-GMFNet: Fourier Kolmogorov-Arnold-based Group Multi-scale Fusion Network for Aneurysm image Segmentation

FKAN-GMFNet: Fourier Kolmogorov-Arnold-based Group Multi-sca...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Shanchen Pang Xue Zhao Yulin Zhang Yawu Zhao Hengtao Ding Zhiyuan Zhao Sibo Qiao College of Computer Science and Technology Qingdao Institute of Software China University of Petroleum (East China) Qingdao China College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao China The College of Software Tiangong University Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

KAN-based networks, while offering improved interpretability compared to traditional models used in medical image segmentation, often struggle with limited adaptability to diverse imaging environments, making them less ideal for such tasks. To address this issue, we propose a Fourier Kolmogorov–Arnold–based (FKAN) Group Multi–scale Fusion Network, termed FKAN–GMFNet, which incorporates an FKAN layer into a labeled intermediate representation, introducing an Enhanced–FKAN block. Furthermore, we develop the Attention Group Multi–scale Aggregation (ATGMA) module, which leverages attention mechanisms and grouping strategies to effectively fuse feature masks with both high– and low–scale feature information, thereby achieving a comprehensive multi-scale feature representation. Extensive experiments demonstrate that the FKAN GMFNet significantly outperforms seven state–of–the–art methods in both Dice and IoU scores, where the Dice and IoU scores for the IAS–L dataset are 88.82% and 80.09%, respectively. Code is available at https://***/zx123868/FKAN-GMFNet.

关键词： image segmentation Adaptation models Codes Attention mechanisms Fuses Aneurysm signal processing Acoustics Speech processing Biomedical imaging

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Optimisation Strategies for End-To-End Transformer Model for Target Detection in Low Light Environments

Optimisation Strategies for End-To-End Transformer Model for...

引用

Intelligent systems and Computational Networks (ICISCN), international conference on

作者： Zhengxuan Ren Jiangsu University of Science and Technology Zhenjiang China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Devices like surveillance in low-light environments are much more difficult for motion target capture than when there is sufficient light. In order to ensure accurate detection of moving targets in low light environments, an optimization strategy for end-to-end Transformer model for target detection in low light environments is proposed. End-to- end Transformer model, capturing long distance targets through multi-head self-attention mechanism and parallel computation of position coding. By constructing a low resolution image sub-block mean image to increase the signal-to-noise ratio and target resolution, optimising the approximate median filtering background model, determining the binary image threshold for moving targets, and optimising the approximate median filtering biscale background model to obtain a more accurate target contour, we achieve the optimisation of target detection in low light environments at night based on the end- to-end Transformer model. The results show that the filtered mAP results using the method in this paper are 1.0% better than the comparison method, and the detection accuracy has been maintained above 93%. This method is beneficial for improving the performance of end-to-end object detection, with better detection accuracy, laying a solid foundation for assisting law enforcement work of public security organs.

关键词： image segmentation Accuracy image resolution Computational modeling Surveillance Object detection Transformers Solids Optimization signal resolution

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image-assisted Label Connective Completion for Vessel Segmentation with Insufficient Annotations

Image-assisted Label Connective Completion for Vessel Segmen...

引用

international conference on Acoustics, Speech, and signal Processing (ICASSP)

作者： Xiaoqi Zheng Baoyao Yang Xiuwen Fang Wenfang Yao Mang Ye School of Computer Science and Technology Guangdong University of Technology School of Computer Science Wuhan University School of Nursing The Hong Kong Polytechnic University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Automatic and accurate vessel segmentation is crucial for disease diagnosis. Deep learning methods are widely used, but their promising results rely on accurately annotated data. Due to complex vessel morphology and low-contrast image, accurate vessel delineation poses a practical challenge, resulting in insufficient annotations, which is a prominent form of noisy labels. This paper proposes an image-assisted Label Connective Completion method, which enhances label’s vessel information by images under the supervision of connectivity to address insufficient annotation issue. Specifically, we develop an image-guided Vessel Enhancement module, which transmits structural information extracted from images based on label navigation to label space, promoting completion of missing annotated parts in original labels. In addition, a branch completion-connectivity loss is designed and introduced as an auxiliary supervision to prevent vessel branch disconnection during label completion. Experimental results on DRIVE, CHASE DB1 and DCA1 datasets demonstrate that our method outperforms existing noisy labels learning methods.

关键词： Learning systems image segmentation Accuracy Annotations Navigation Morphology signal processing Noise measurement Medical diagnosis Speech processing

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