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SMUG: Towards Robust Mri Reconstruction by Smoothed Unrolling

SMUG: Towards Robust Mri Reconstruction by Smoothed Unrollin...

引用

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

作者： Hui Li Jinghan Jia Shijun Liang Yuguang Yao Saiprasad Ravishankar Sijia Liu Huazhong University of Science and Technology China Michigan State University East Lansing MI USA

Although deep learning (DL) has gained much popularity for accelerated magnetic resonance imaging (MRI), recent studies have shown that DL-based MRI reconstruction models could be over-sensitive to tiny input perturbations (that are called ‘adversarial perturbations’), which cause unstable, low-quality reconstructed images. This raises the question of how to design robust DL methods for MRI reconstruction. To address this problem, we propose a novel image reconstruction framework, termed SMOOTHED UNROLLING (SMUG), which advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust learning operation. RS, which improves the tolerance of a model against input noises, has been widely used in the design of adversarial defense for image classification. Yet, we find that the conventional design that applies RS to the entire DL process is ineffective for MRI reconstruction. We show that SMUG addresses the above issue by customizing the RS operation based on the unrolling architecture of the DL-based MRI reconstruction model. Compared to the vanilla RS approach and several variants of SMUG, we show that SMUG improves the robustness of MRI reconstruction with respect to a diverse set of perturbation sources, including perturbations to input measurements, different measurement sampling rates, and different unrolling steps. Code for SMUG will be available at https://***/LGM70/SMUG.

关键词： Deep learning Smoothing methods Magnetic resonance imaging Perturbation methods Design methodology signal processing Robustness

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Mix-Mask Augmentation and Self-Reconstruction for Cross-Domain Few-Shot Hyperspectral image Classification

Mix-Mask Augmentation and Self-Reconstruction for Cross-Doma...

引用

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

作者： Qin Xu Jie Wei Qihang Wu Jiahui Wang Xiao Wang Jinpei Liu Bo Jiang School of Computer Science and Technology Anhui University Hefei China School of Business Anhui University Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, the metric-based prototypical methods achieves promising performance in few-shot learning (FSL) for hyperspectral image (HSI) classification. However, the existing models are easily affected by the noisy pixels of different categories around the center pixel of the patch, and tend to focus on the most representative features while ignoring other important ones, which cause the overfitting problem. Moreover, the commonly used dimension reduction operation of the feature of source and target domains inevitably results in the loss of valuable spectral information. To address these issues, we propose the mix-mask augmentation and self-reconstruction for cross-domain HSI classification. The pixel mask augmentation is introduced to enhance the sample diversity of query set and suppress the impact of noisy pixels, thus encouraging the model to discover discriminative features on a wider range. The CutMix augmentation is also adopted to generate the mixed support set and mixed prototypes, mitigating the negative impact of confusing prototypes. Furthermore, we develop the self-reconstruction module which can preserve more useful feature information during the dimension reduction for feature representation of the source and target domains. Extensive experiments on three public HSI datasets demonstrate that the proposed method achieves superior performance with fewer computational costs in comparison with the SOTA methods.

关键词： Dimensionality reduction Prototypes signal processing Encoding Noise measurement Speech processing Few shot learning Hyperspectral imaging Overfitting image classification

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Semantic-Aware Prompt Learning for Multimodal Sarcasm Detection

Semantic-Aware Prompt Learning for Multimodal Sarcasm Detect...

引用

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

作者： Guangjin Wang Bao Wang Fuyong Xu Zhenfang Zhu Peipei Wang Ru Wang Peiyu Liu School of Information Science and Engineering Shandong Normal University Jinan China School of Information Science and Electrical Engineering Shandong Jiao Tong University Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal sarcasm detection aims to identify whether utterances express sarcastic intentions contrary to their literal meaning based on multimodal information. However, existing methods fail to explore the model’s "ability to understand" the semantics expressed by sentences in the image context from semantic diversity perspectives. In this paper, we propose a multi-view semantic awareness method, which concretizes semantics from multiple perspectives to improve the model’s ability to capture different semantic features. Specifically, two learnable prefixes are attached to the text representation respectively to construct semantic representations from both the literal meaning and sarcastic intention perspectives. Then, image-text information is further fused through cross-attention to guide the semantic representation of different perspectives in the image context. Finally, the semantics expressed by prefixes are strengthened through KL divergence, thereby encouraging the model to capture two distinctive semantic features. Experiments on benchmark datasets demonstrate the effectiveness of our method.

关键词： Semantics Benchmark testing Speech enhancement signal processing Vectors Acoustics Context modeling

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Effective Adversarial Sample Detection for Securing Automatic Speech Recognition

Effective Adversarial Sample Detection for Securing Automati...

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ieee conference on Advanced Video and signal based Surveillance (AVSS)

作者： Chih-Yang Lin Yan-Zhang Wang Shou-Kuan Lin Isack Farady Yih-Kuen Jan Wei-Yang Lin Department of Mechanical Engineering National Central University Taiwan Rehabilitation Engineering Lab University of Illinois at Urbana-Champaign USA Department of Computer Science and Information Engineering National Chung Cheng University Taiwan Yoder International Academy Taoyuan Taiwan Rehabilitation Engineering Lab College of Applied Health Sciences University of Illinois at Urbana-Champaign USA

ISBN: (数字)9798350374285

ISBN: (纸本)9798350374292

Deep learning has emerged as a pivotal technology across various domains, demonstrating remarkable performance. However, its susceptibility to security threats, particularly adversarial samples, poses a significant concern. These samples can manipulate inputs slightly, deceiving models such as those used in image or speech recognition and potentially leading to incorrect predictions. This undermines the reliability of deep learning in critical applications. In this paper, we propose an effective method utilizing Autoencoder to detect and intercept audio adversarial attacks before they are input to speech recognition models. The proposed approach first uses clean audio data to train the Autoencoder model, then isolates adversarial samples from clean ones by comparing their features against normal features encoded in the Autoencoder. Our method does not require prior knowledge about the target automatic speech recognition model or attack methods. Experimental results show that it can effectively discriminate adversarial attack samples from clean ones with high accuracy.

关键词： Deep learning Computational modeling Surveillance Scalability Predictive models Phonetics Feature extraction

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3D Visual Cable Intelligent Control System based on Multidimensional Information Perception of internet of Things

3D Visual Cable Intelligent Control System Based on Multidim...

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2022 international conference on Artificial Intelligence of Things and Crowdsensing (AIoTCs)

作者： Jiong Zhao Junbing Zhang Hao Li Guoqiang Lei Jun Zhao Taiyuan Power Supply Company of State Grid Shanxi Electric Power Company Taiyuan Shanxi China

With the development of society, cable plays an increasingly important role in people's daily life. Multidimensional information perception technology, as a new era information technology, has been applied to all walks of life. This paper aims at real-time monitoring of cables based on 3D visualization of the internet of Things. Firstly, the theoretical basis and idea of intelligent control are introduced. Secondly, the overall scheme design of the system, the selection of key hardware and the data acquisition and transmission modules are described. Finally, starting from the communication mode, a variety of signal fusion methods are proposed to better realize the comprehensive utilization of multi-source information. The feasibility and effectiveness of the proposed technology in practical application are verified through simulation. The verification results show that, The control time of the 3D visualization cable intelligent control system based on the multi-dimensional information perception of the internet of Things is within 6 seconds, the temperature of the intelligent control cable fluctuates around 25 °C, and the monitoring image can achieve high-definition image quality, which shows that the technology can meet the user's intelligent control needs.

关键词： Wireless communication Visualization Three-dimensional displays Visual systems Real-time systems Temperature control internet of Things

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Optimizing Learned image Compression On Scalar and Entropy-Constraint Quantization

Optimizing Learned Image Compression On Scalar and Entropy-C...

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ieee international conference on image Processing

作者： Florian Borzechowski Michael Schäfer Heiko Schwarz Jonathan Pfaff Detlev Marpe Thomas Wiegand Fraunhofer Institute for Telecommunications - Heinrich Hertz Institute Institute of Computer Science Free University of Berlin Germany Department of Telecommunication Systems Technical University of Berlin Germany

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

The continuous improvements on image compression with variational autoencoders have lead to learned codecs competitive with conventional approaches in terms of rate-distortion efficiency. Nonetheless, taking the quantization into account during the training process remains a problem, since it produces zero derivatives almost everywhere and needs to be replaced with a differentiable approximation which allows end-to-end optimization. Though there are different methods for approximating the quantization, none of them model the quantization noise correctly and thus, result in suboptimal networks. Hence, we propose an additional finetuning training step: After conventional end-to-end training, parts of the network are retrained on quantized latents obtained at the inference stage. For entropy-constraint quantizers like Trellis-Coded Quantization, the impact of the quantizer is particularly difficult to approximate by rounding or adding noise as the quantized latents are interdependently chosen through a trellis search based on both the entropy model and a distortion measure. We show that retraining on correctly quantized data consistently yields additional coding gain for both uniform scalar and especially for entropy-constraint quantization, without increasing inference complexity. For the Kodak test set, we obtain average savings between $1 \%$ and $2 \%$, and for the TecNick test set up to $2.2 \%$ in terms of Bjøntegaard-Delta bitrate.

关键词： Training Quantization (signal) image coding Runtime Noise Training data Rate-distortion

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Gaze-Assisted Human-Centric Domain Adaptation for Cardiac Ultrasound image Segmentation

Gaze-Assisted Human-Centric Domain Adaptation for Cardiac Ul...

引用

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

作者： Ruiyi Li Yuting He Rongjun Ge Chong Wang Daoqiang Zhang Yang Chen Shuo Li College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics China School of Computer Science and Engineering Southeast University China School of Instrument Science and Engineering Southeast University China Department of Biomedical Engineering Case Western Reserve University USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Domain adaptation (DA) for cardiac ultrasound image segmentation is clinically significant and valuable. However, previous domain adaptation methods are prone to be affected by the incomplete pseudo label and low-quality target to source images. Human-centric domain adaptation has great advantages of human cognitive guidance to help model adapt to target domain and reduce reliance on labels. Doctor gaze trajectories contains a large amount of cross-domain human guidance. To leverage gaze information and human cognition for guiding domain adaptation, we propose gaze-assisted human-centric domain adaptation (GAHCDA), which reliably guides the domain adaptation of cardiac ultrasound images. GAHCDA includes following modules: (1) Gaze Augment Alignment (GAA): GAA enables the model to obtain human cognition general features to recognize segmentation target in different domain of cardiac ultrasound images like humans. (2) Gaze Balance Loss (GBL): GBL fused gaze heatmap with outputs which makes the segmentation result structurally closer to the target domain. The experimental results show that our proposed framework is able to segment cardiac ultrasound images more effectively in the target domain than GAN-based methods and other self-train based methods and shown great potential in clinical application.

关键词： Heating systems image segmentation Adaptation models Ultrasonic imaging Target recognition Gallium arsenide signal processing Cognition Trajectory Speech processing

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A Robust Multi-source image Matching Method for Power Equipment based on Improved PIIFD

A Robust Multi-source Image Matching Method for Power Equipm...

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ieee international conference on signal and image Processing (ICSIP)

作者： Zhengbing Wang Xugang Feng Dan Li Zechen Zhang Yuan Zhao Guili Xu Anhui Province Key Laboratory of Special Heavy Load Robot School of Electrical and Information Engineering Anhui University of Technology Ma’anshan China College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

Robust feature matching for multi-source images of power equipment is critical for automatic diagnosis of power grid. However, classical image matching methods may not be suitable for this challenging task due to different resolution, spectrum and viewpoint between multi-source images. To solve this problem, a robust multi-source image matching algorithm is presented. First, the maximum moment map of phase congruency of input image is computed to enhance the raw image. Then, Canny operator and contour tracking method are employed to detect contour map of enhanced image. A set of feature points on contours is extracted by the curvature scale space corner detector. Second, a robust method is presented to assign main orientation to each feature point based on the local contour information, and a scale-invariant PIIFD descriptor is computed for feature point description. Finally, the bidirectional matching procedure is implemented to achieve feature correspondence and matching results are refined by both geometric and photometric information. We conduct the experiments on pairs of visible and infrared images of power equipment, and the results can demonstrate the effectiveness of our methods.

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Enhancing Facial Expression Recognition Through an Innovative Feature Selection Technique based on Fisher Criterion and Genetic Algorithm

Enhancing Facial Expression Recognition Through an Innovativ...

引用

ieee Information technology, Networking, Electronic and Automation Control conference

作者： Li Song Jie Huang Qi Sun Qianfeng Jiang Chao Du School of Industrial Internet Beijing Information Technology College Beijing China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

Facial Expression Recognition (FER) is a pivotal and captivating field in computer vision with broad applications that have made it a popular research topic since the 1990s, playing a significant role in image processing. FER typically involves two key stages: feature extraction and selection, followed by classification. This study introduces a novel feature selection approach, named FCGA (Fisher Criterion and Genetic Algorithm), to address challenges in FER. In the FCGA method, the Fisher criterion is used to evaluate feature subsets, and a genetic algorithm is devised to identify an optimal feature subset from the pool of candidate. The FCGA method is applied to select features for FER from various types, such as Local Phase Quantization (LPQ) and Pyramid of Histogram of Oriented Gradients (PHOG). These selected features then collaborate with Support Vector Machine classifiers to categorize facial expressions. Experimental results utilizing the JAFFE and FER2013 databases reveal that the FCGA method boosts the recognition rate by a noteworthy margin of up to 1.3% and 3.2% compared to other feature selection methods.

关键词： Support vector machines Measurement Accuracy Quantization (signal) Face recognition Feature extraction Genetics Computational efficiency Optimization Genetic algorithms

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Efficient-USR: Prompt Guided Dual-Domain Feature Information for Efficient Underwater image Super-Resolution

Efficient-USR: Prompt Guided Dual-Domain Feature Information...

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

作者： Alik Pramanick Utsav Bheda Arijit Sur Department of Computer Science and Engineering Indian Institute of Technology Guwahati India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advances in deep learning have significantly improved underwater image super-resolution (UISR) performance. However, their large and complex architectures result in huge computational complexity, making them unsuitable for low-power devices such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs). In addition, current research emphasizes designing deep models to enhance performance but overlooks the potential benefits of integrating frequency domain information. To overcome these challenges, we propose Efficient-USR, an effective dual-domain information-based lightweight framework for UISR. In Efficient-USR, we analyze the image in different scales by incorporating two core components: a) spatial-frequency interaction block (SFIB) to capture both the global and local context by operating spatial-channel cross-attention on spatial-frequency features and b) prompt-guided cross-attention block (PCAB) to efficiently encode degradation information in prompt component and perform feature fusion of multiple scales via cross-attention. Extensive experiments on two UISR benchmarks demonstrate the effectiveness of our Efficient-USR (e.g., ∼ 1.35% and ∼ 1.40% SSIM improvement on UFO-120 and USR-248 4 datasets, respectively, with only 0.18 M parameters). The code× is available at: https: //***/Alik033/Efficient-USR.

关键词： Performance evaluation Degradation Visualization Remotely guided vehicles Message passing Frequency-domain analysis Superresolution Speech processing Spatial resolution signal resolution

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