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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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Gait Deviation Assessment: From signal to image Analysis

Gait Deviation Assessment: From Signal to Image Analysis

引用

Workshops on image processing Theory, Tools and applications, IPTA

作者： Lorenzo Hermez Nesma Houmani Sonia Garcia-Salicetti Omar Galarraga Vincent Vigneron SAMOVAR Télécom SudParis Institut Polytechnique de Paris Palaiseau France Movement Analysis Laboratory UGECAM Ile-de-France Coubert France Informatique Bio-Informatique et Systèmes Complexes (IBISC) EA 4526 Université Paris-Saclay Evry France

ISBN: (数字)9798331541842

ISBN: (纸本)9798331541859

In this paper, we propose a novel framework for gait quality assessment based on image analysis, extending the traditional signal-based approach. Specifically, we construct Cycle Dissimilarity images (CDI) from raw gait signals. Such images summarize all local dissimilarities existing in the dynamics between a gait signal and one normal gait reference. Also, we construct a typical dissimilarity image, by matching each normal gait reference to itself. Then, we propose to quantify gait deviations by computing the distance between the CDIs and the typical dissimilarity images. Our results indicate that, compared to the signal-based approach, this new framework leads to a more precise gait deviation assessment, and a more refined characterization of motor impairments, as hemiparesis, tetraparesis, and paraparesis.

关键词： image analysis Motors Quality assessment

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Robust and Efficient Adversarial Defense in SNNs via image Purification and Joint Detection

Robust and Efficient Adversarial Defense in SNNs via Image P...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Weiran Chen Qi xu School of Microelectronics University of Science and Technology of China Hefei

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spiking neural networks (SNNs) leverage neural spikes to provide solutions for low-power intelligent applications on neuromorphic hardware. Although the spiking mechanism significantly enhances computational efficiency, especially in energy-constrained environments, they still lack resistance to noise perturbations and adversarial attacks. In this paper, we propose a defense framework based entirely on SNNs and design a fast and efficient training algorithm. The framework is divided into an image purification module and an adversarial detection module. The image purification module is employed for the extraction of noise and the reconstruction of input images. The adversarial detection module is utilized to differentiate between clean and adversarial images, thereby further enhancing defense performance. Meanwhile, our approach is highly flexible and can be seamlessly integrated with other defense strategies. Experimental results demonstrate that the proposed methodology outperforms state-of-the-art baselines in terms of defense effectiveness, training time and resource consumption.

关键词： Training Resistance Purification Perturbation methods Noise signal processing algorithms Spiking neural networks Robustness Computational efficiency Speech processing

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An Iterative Method for image Denoising Based on Ramanujan Sums 2

An Iterative Method for Image Denoising Based on Ramanujan S...

引用

2nd IEEE International conference on Intelligent Technologies, CONIT 2022

作者： Jaiswal, Priyansh Ansari, Noman Ahmed Mandpura, Anup Kumar Pandey, Neeta Delhi Technological University Department of Electrical Engineering New Delhi India Delhi Technological University Department of Electronics and Communication New Delhi India

ISBN: (数字)9781665484077

ISBN: (纸本)9781665484077

The suppression of noise in the received noisy image is termed as image denoising. Recently Ramanujan Sums has garnered a lot of attention in signal processing applications due to its attributes such as: integer valued, symmetry and periodicity. This paper presents an iterative image denoising technique based on Ramanujan Sums. Further edge detection using the Ramanujan Sums is performed in conjunction to image denoising to improve the over-all image quality. The proposed iterative image denoising algorithm has better structural similarity index (SSI) in comparison to a 3x3 averaging filter. Simulation results also show that the residual error in each iteration decreases with increase in iterations. © 2022 IEEE.

关键词： image denoising

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Improved image Classification with Manifold Neural Networks

Improved Image Classification with Manifold Neural Networks

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Caio F. Deberaldini Netto Zhiyang Wang Luana Ruiz Department of Applied Mathematics and Statistics Mathematical Institute for Data Science (MINDS) Data Science and Artificial Intelligence Institute (DSAI) Johns Hopkins University Baltimore USA Department of Electrical and Systems Engineering University of Pennsylvania Philadelphia USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Graph Neural Networks (GNNs) have gained popularity in various learning tasks, with successful applications in fields like molecular biology, transportation systems, and electrical grids. These fields naturally use graph data, benefiting from GNNs’ message-passing framework. However, the potential of GNNs in more general data representations, especially in the image domain, remains underexplored. Leveraging the manifold hypothesis, which posits that high-dimensional data lies in a low-dimensional manifold, we explore GNNs’ potential in this context. We construct an image manifold using variational autoencoders, then sample the manifold to generate graphs where each node is an image. This approach reduces data dimensionality while preserving geometric information. We then train a GNN to predict node labels corresponding to the image labels in the classification task, and leverage convergence of GNNs to manifold neural networks to analyze GNN generalization. Experiments on MNIST and CIFAR10 datasets demonstrate that GNNs generalize effectively to unseen graphs, achieving competitive accuracy in classification tasks.

关键词： Manifolds Electric potential High dimensional data Autoencoders Transportation signal processing Graph neural networks Molecular biology Speech processing image classification

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AI-generated image detectors are surprisingly easy to mislead… for now

AI-generated image detectors are surprisingly easy to mislea...

引用

Asia-Pacific signal and Information processing Association Annual Summit and conference (APSIPA)

作者： Zihang Lyu Jun xiao Cong Zhang Kin-Man Lam The Hong Kong Polytechnic University Kowloon Hong Kong

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

AI-generated image detectors, also known as fake image detectors, have demonstrated remarkable performance across different datasets and generators, achieving superior detection accuracy and generalizability. Considering the structures of existing AI-generated detectors, which perform binary classification, we propose a simple yet effective adversarial attack method, namely Binary Fast Gradient Sign Method (BFGSM), in this paper. We demonstrate that existing AI-generated image detectors are sensitive to subtle and imperceptible distortions, which raises serious safety risk for these models and makes them inadequate for real-world applications. Experimental results show that our proposed attack successfully misleads current AI-generated image detectors, reducing the attack distortion level by 7.72% with negligible impact on the misleading success rate.

关键词： Privacy Perturbation methods Asia Detectors Information processing Distortion Generators Safety Security Noise level

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Multi-channel image Fusion in Ultraviolet Dynamic Detection on Electric Railways 13

Multi-channel Image Fusion in Ultraviolet Dynamic Detection ...

引用

13th International conference on Graphics and image processing (ICGIP)

作者： Wang, Yan-guo Fang, Jun Qi, Liusheng xie, Dapeng Li, Liandong Gu, Zichen Liu, Guoyue Tao, Kai Sheng, Liang China Acad Railway Sci Corp Ltd Inst Infrastruct Inspect Beijing Peoples R China China Railway Wuhan Grp Corp Ltd Beijing Peoples R China INMAI Railway Technol Corp Ltd Beijing Peoples R China

ISBN: (数字)9781510650435

ISBN: (纸本)9781510650435;9781510650428

Catenary is an important infrastructure along electric railways, which should be periodically checked to guarantee the normal working status of power supply for the trains. Technologies of ultraviolet detection has been introduced into catenary inspection, and it can be used to detect the electrical state for insulators that are the isolation components between high voltage and low voltage in catenary. However, due to the dynamic detection condition with the running of catenary inspection car, the performance of multi-channel image fusion becomes a key issue in the application on electric railways. In this paper, we proposed a multi-channel image fusion method for the ultraviolet and visible image in dynamic detection on electric railways. The ultraviolet detection module is designed with dual optical coupling alignment, which guarantees the consistency of multi-channel in spatial domain. The post detection is adopted and used as an external trigger signal for both the ultraviolet and visible cameras, which are working in triggered acquisition mode with good time synchronization. Finally, insulator localization algorithms based on deep learning are utilized in the automatic detection of insulator area in visible image, and the ultraviolet signals are successfully extracted from the image fusion results. The performance of multi-channel image fusion method proposed in the paper has been validated, in both lab test and dynamic test on electric railways.

关键词： Visual inspection ultraviolet detection sensor applications and deployments image and video acquisition

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TriFormer: Triple Branch Heterogeneous Transformers for UHD image Restoration

TriFormer: Triple Branch Heterogeneous Transformers for UHD ...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： xinchen Ma Yue Lu School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Ultra-high-definition (UHD) image restoration is becoming a critical research area due to the increasing demand for high-quality visual content in various applications, including autonomous driving, remote sensing, digital entertainment, etc. However, UHD image restoration tasks present notable challenges, including the significant computational burden posed by the large image size scale, the difficulty in preserving fine details and textures, and the increased susceptibility to noise and artifacts during the restoration process. In this paper, we propose a novel triple-branched optimized heterogeneous transformer for UHD image restoration, named TriFormer. Specifically, our method embeds a high-resolution CNN branch for high-frequency features, a low-resolution pixel-wise attention branch for low-frequency features, and a channel-wise attention branch for feature fusion. We perform experiments across two UHD image restoration tasks: enhancing low-light images and deblurring. Results demonstrate that our model outperforms state-of-the-art approaches both quantitatively and qualitatively. The code will be made available at https://***/Chloe-mxxxxc/TRIFORMER.

关键词： Visualization Technological innovation Noise Graphics processing units Entertainment industry Computer architecture Transformers image restoration Speech processing Remote sensing

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The Role of Virtual Reality Technology in Knowledge Acquisition: A Systematic Review

The Role of Virtual Reality Technology in Knowledge Acquisit...

引用

International conference on Advanced Technologies for signal and image processing (ATSIP)

作者： Musaab Osamah Anwer Ezzeddine Zagroubaa LIMTIC Laboratory Higher Institute of Computer Science University of Tunis El-Manar Ariana Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

This review delves into the use of Virtual Reality (VR) technology for knowledge acquisition, focusing on three main key areas: VR in education, VR for exploring biological data, and VR in games design. The other focus of this work is on how to assess VR software applications and the metrics involved. These aspects are chosen for their significance in the VR research field. The review covers various education-based VR applications, discussing their advantages and limitations. It also examines how VR technology can be used to visualize biological data and create interactive network models and highlighting both the benefits and challenges. The main features that enable users to interact with the virtual environment in games are also illustrated in this work. Additionally, the review emphasizes the importance of metrics in evaluating VR software, which is currently lacking in this area. Finally, the review presents an overview of current research directions in VR literature.

关键词： Solid modeling Reviews Knowledge acquisition image processing Education Virtual environments Focusing

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A novel Grasshopper optimized ResU-Net for Brain Tumor Segmentation

A novel Grasshopper optimized ResU-Net for Brain Tumor Segme...

引用

IEEE International conference on signal and image processing applications (ICSIPA)

作者： Nishtha Tomar Prakhar Bhatt Gaurav Bhatnagar Indian Institute of Technology Jodhpur Jodhpur India

ISBN: (数字)9798350352368

ISBN: (纸本)9798350352375

Brain tumors pose significant health risks, making accurate segmentation from MRI images critical for effective diagnosis, treatment planning, and monitoring. Traditional manual segmentation methods are often prone to errors, prompting the development of advanced architectures such as U-Net. However, U-Net faces challenges, including vanishing gradients and a high number of hyperparameters that require careful tuning. To address these limitations, we propose the Grasshopper Optimized ResU-Net model, which incorporates residual blocks within the optimized U-Net framework. This novel architecture enhances brain tumor segmentation by effectively learning complex features from MRI scans while optimizing performance through Grasshopper Optimization. Extensive experimental evaluations demonstrate that the proposed model significantly outperforms existing methods in brain tumor segmentation, highlighting its efficacy in clinical applications.

关键词： image segmentation Magnetic resonance imaging Manuals Brain modeling Robustness Planning Tuning

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