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G-Depth: An Efficient Graph Method for Robust Depth Completion

G-Depth: An Efficient Graph Method for Robust Depth Completi...

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

作者： Zhongyu Huang Yijun Chen Aizierjiang Aiersilan Li Li University of Macau Macau SAR China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Depth completion has played a vital role in enhancing depth perception in various applications such as autonomous driving, robotics, and 3D reconstruction. It is a critical task that involves generating a dense depth map from an RGB image and an aligned sparse depth map. However, many existing studies simply assume the depth map to be readily available, overlooking the fact that depth maps are often missing or subject to perturbations in real-world scenarios. The absence of these data can mislead the network into overfitting incorrect patterns, ultimately diminishing the performance of depth completion models. To tackle these challenges, we propose G-Depth, a method that innovatively involves Graph Neural Networks (GNNs) into a two-branch backbone. By integrating the graph convolutional mechanism into the deep layers of the network, G-Depth achieves an expanded receptive field, enabling a more accurate understanding and capture of spatial relationships between objects. To enhance robustness, we also propose a novel Enhanced Data Distillation(EDD) method to address the issue of modality absence, mitigating its negative effects, achieving a 73.2% average improvement in RMSE without sparse depth input. Comprehensive experiments on KITTI dataset demonstrate the effectiveness and robustness of the proposed G-Depth.

关键词： Accuracy Three-dimensional displays Convolution Perturbation methods Computer architecture Robustness Real-time systems Speech processing Optimization Overfitting

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Domain Generalized Gaze Estimation Network for Enhanced Cross-Environment Performance

Domain Generalized Gaze Estimation Network for Enhanced Cros...

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IEEE International conference on Information Communication and signal processing (ICICSP)

作者： Yaoyao Mo Yuhang Hong Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

Gaze estimation technology provides a non-invasive and natural interaction method by tracking the direction of a person's gaze, making it valuable for various applications. However, existing gaze estimation models are often optimized for specific environments, and their performance tends to degrade significantly when applied to new or different settings. This decline in performance is primarily due to substantial variations in factors such as lighting conditions, ethnicity, gender, and age between different environments, resulting in insufficient generalization capabilities of the models. To address the aforementioned challenges, we propose a novel gaze estimation network, GE-Net, which significantly enhances the cross-environment performance of eye-tracking models through domain generalization. The network integrates Frequency Domain Data Augmentation (FDDA) with a lightweight Multilayer Perceptron (LightMLP), allowing it to learn gaze-consistent features without the need for target domain data sampling. The architecture of GE-Net comprises a feature extraction backbone (ResNet18), an image restoration module, and the innovative LightMLP. By perturbing gaze-irrelevant features in the frequency domain, the model demonstrates improved generalization capabilities across different datasets. Experimental results on four cross-domain datasets demonstrate that GE-Net exhibits superior generalization capabilities compared to existing methods, even in the absence of target domain samples. The network's efficiency and adaptability make it a promising tool for applications requiring reliable gaze estimation. The implementation code can be found at https://***/yuhanghong123/GENet.

关键词： Adaptation models Frequency-domain analysis Estimation Lighting Gaze tracking Interference Multilayer perceptrons Feature extraction image restoration Reliability

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CTGDiff: A Conditional Diffusion Model for Cardiotocography signal Synthesis

CTGDiff: A Conditional Diffusion Model for Cardiotocography ...

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

作者： xiaoqing Li Pufan Cai Yu Lu Shijie Shi Liangkun Ma xianghua Fu College of Big Data and Internet Shenzhen Technology University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Surgery Yong Loo Lin School of Medicine National University of Singapore Singapore School of Applied Technology Shenzhen University Shenzhen China Department of Obstetrics and Gynecology Peking Union Medical College Hospital Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The analysis of Cardiotocography (CTG) signals is often hindered by challenges such as limited data availability and label imbalance, which can undermine the performance of deep learning models. To address these issues, we present CTGDiff, a novel conditional diffusion model designed for generating synthetic Fetal Heart Rate (FHR) and Uterine Contraction (UC) signals. CTGDiff leverages both Phase-Rectified signal Averaging (PRSA) spectrograms and UC as conditioning inputs for FHR, and integrates time encoding, condition generation from PRSA features, and residual blocks with dilated convolutions to capture both temporal dynamics and long-range dependencies. Extensive experiments, both qualitative and quantitative, demonstrate the model’s ability to synthesize high-quality CTG signals. In comparison with GANs and image-based diffusion models, CTGDiff achieves superior signal fidelity and distribution similarity for FHR, as indicated by metrics such as a 0.004 maximum mean deviation (MMD), 0.646 percent root mean square difference (PRD), 3.951 relative entropy (RE), and 0.291 Frechet distance (FD). Expert evaluations confirm that the model can generate both normal and abnormal CTG signals with high accuracy, conditioned on specific input data. These results underscore the potential of diffusion models for a wide range of applications in biomedical time series analysis, including signal synthesis, imputation, and noise reduction.

关键词： Measurement Fetal heart rate Biological system modeling Time series analysis Diffusion models Data models signal synthesis Cardiography Speech processing Spectrogram

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Quantization Effects on Neural Networks Perception: How would quantization change the perceptual field of vision models?

Quantization Effects on Neural Networks Perception: How woul...

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Workshops on image processing Theory, Tools and applications, IPTA

作者： Mohamed Amine KERKOURI Marouane TLIBA Aladine ChETOUANI Alesssandro Bruno Université d'Orléans Orléans France

ISBN: (数字)9798331541842

ISBN: (纸本)9798331541859

Neural network quantization is a critical technique for deploying models on resource-limited devices. Despite its widespread use, the impact of quantization on model perceptual fields, particularly in relation to class activation maps (CAMs), remains underexplored. This study investigates how quantization influences the spatial recognition abilities of vision models by examining the alignment between CAMs and visual salient objects maps across various architectures. Utilizing a dataset of 10,000 images from imageNet, we conduct a comprehensive evaluation of six diverse CNN architectures: VGG16, ResNet50, EfficientNet, MobileNet, SqueezeNet, and DenseNet. Through the systematic application of quantization techniques, we identify subtle changes in CAMs and their alignment with Salient object maps. Our results demonstrate the differing sensitivities of these architectures to quantization and highlight its implications for model performance and interpretability in real-world applications. This work primarily contributes to a deeper understanding of neural network quantization, offering insights essential for deploying efficient and interpretable models in practical settings.

关键词： Performance evaluation Visualization Quantization (signal) Sensitivity Systematics Neural networks Semantics Robustness Cams Residual neural networks

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A generative adversarial network for ultrasound signal enhancement: transforming low-voltage, beam-formed, radio frequency data to high-voltage data

A generative adversarial network for ultrasound signal enhan...

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conference on Medical Imaging - Ultrasonic Imaging and Tomography

作者： Asiedu, Mercy N. Benjamin, Alex R. Singh, Vivek K. Wang, Shuhang W. Wu, Kevin Samir, Anthony E. Kumar, Viksit S. MIT 77 Massachusetts Ave Cambridge MA 02139 USA Massachusetts Gen Hosp Ctr Ultrasound Res & Translat 101 Merrimac St Boston MA 02114 USA Harvard Med Sch Cambridge MA 02115 USA

ISBN: (纸本)9781510649521;9781510649514

Ultrasound image quality is strongly dependent on penetration depth and attenuation. The transmit voltage in ultrasonic systems can be increased to increase output power and improve the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in deeper regions. However, the utility of using high transmit voltages and thus high output power is limited due to associated thermal and mechanical bioeffects. Additionally, the ability to increase the output power is limited in portable and low-cost ultrasound devices which have lower power. We propose a software-based approach, using a conditional generative adversarial network (cGAN) to amplify signals in deeper regions and enhance image quality without increasing the transmit voltage. The cGAN was customized and trained with beamformed radio frequency phantom data pairs (n=288) acquired with a Verasonics Vantage System;with input data taken at a low output voltage (20V) and corresponding output data taken at a high output voltage (70V). We trained and tested the performance of different loss functions and cGAN architectures. Our proposed model, tested on a hold-out phantom data set (n=73) was able to improve the average penetration depth by roughly 16% (1 cm gain in penetration depth) when compared to the low-voltage images. We found an average increase in CNR of 160.45%+/- 117.64, increase in Peak SNR of 5675%+/- 1.89dB, and increase in SNR of 32.68%+/- 4.84 dB, relative to the low voltage images, for selected hyper and hypoechoic regions of interest. This work has potential applications in fetal imaging, where safety guidelines limit transmit voltage significantly, and portable devices.

关键词： Ultrasound imaging generative adversarial networks machine learning signal processing

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Enhanced Interpretability in Learned image Compression via Convolutional Sparse Coding

Enhanced Interpretability in Learned Image Compression via C...

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IEEE International conference on Multimedia and Expo (ICME)

作者： Yiwen Tu Wen Tan Youneng Bao Genhong Wang Fanyang Meng Yongsheng Liang Harbin Institute of Technology Shenzhen Peng Cheng Laboratory Shenzhen Technology University

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Compared to traditional image compression methods, learned image compression (LIC) methods have demonstrated increasingly superior rate-distortion performance. However, LIC networks are often regarded as black boxes, still lacking a theoretical understanding. Sparse coding provides the sparse and interpretable modeling for analyzing or synthesizing natural images in various signal and image processing applications. Therefore, we introduce convolutional sparse coding (CSC) into transform network for enhancing the interpretability of LIC methods. In this paper, we first employ CSC layers to achieve certain theoretical modeling for LIC network, and adopt a weight sharing strategy in encoder-decoder pair and attention mechanism to balance the complexity and performance. Additionally, we analyze the model robustness against data input perturbations and consider the impact of sparsity trade-off parameter in the CSC layer optimization process. Experimental results demonstrate that our method achieves comparable performance with the corresponding baseline, and our model is more robust.

关键词： Convolutional codes Analytical models image coding Attention mechanisms Convolution Perturbation methods Transforms

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DiGradPatch: Black-Box Patch Attacks via Diffusion-Based Double Gradient and Sensitive Distribution Guidance

DiGradPatch: Black-Box Patch Attacks via Diffusion-Based Dou...

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

作者： Yang Wu Jing Liu College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep neural networks have demonstrated vulnerabilities to black-box adversarial patch attacks in image analysis tasks, raising concerns about their robustness in safety-critical applications. Current methods typically rely on randomized search strategies to determine patch locations and apply unrestricted pixel perturbations in the patch area, leading to high query costs and significant visual distortions that reduce imperceptibility. To address these limitations, we propose Black-Box Patch Attacks via Diffusion-Based Double Gradient and Sensitive Distribution Guidance (DiGradPatch), a novel method designed to generate highly imperceptible adversarial samples with minimal query cost. Our approach leverages the prior sensitive distribution of a surrogate model to efficiently locate the patch by maximizing the joint probability distribution between the global model prediction and selected image regions. Furthermore, we introduce the diffusion model into the black-box patch attack framework for the first time. The prior gradient from the diffusion model, in conjunction with the estimated gradient of the target model, is used to guide the direction of perturbations. This allows the generation of adversarial perturbations within the patch area that align with the distribution of natural samples, effectively reducing perceptual distortions. Extensive experiments on the imageNet dataset show that DiGradPatch achieves superior imperceptibility with significantly reduced query costs, maintaining a high attack success rate. Compared to existing methods, our approach requires fewer than 10 queries and achieves an L ∞ norm that is only 1/10 of state-of-the-art techniques, while maintaining the highest attack success rate.

关键词： Visualization Costs Acoustic distortion Perturbation methods Closed box Predictive models Diffusion models Search problems Probability distribution Speech processing

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FCoDT-Net: A Novel Framework for High-Precision Medical image Segmentation Using Contextual Distillation Transformer

FCoDT-Net: A Novel Framework for High-Precision Medical Imag...

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

作者： Qin YuTao Yang SiZhe Hu Bang Ren Wei School of Computer and Information Science Southwest University Chongqing China Chongqing Fu Dan Digital Medical Technology Co. Ltd Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Current methods in medical image semantic segmentation often rely on simple skip connections within U-shaped network structures. These approaches fail to bridge the semantic gap between the encoder and decoder module and do not fully exploit the rich contextual information among. The unused information leads to suboptimal segmentation results. In this paper, we propose the Feature Context Distillation Transformer Network (FCoDT-Net), a deep learning model designed to address these limitations by leveraging the rich contextual information within the skip connections. FCoDT-Net introduces a novel Context Distillation Transformer (CoDT) within its decoder module, which effectively utilizes abundant contextual information from skip connections. CoDT leverages attention matrix calculations between feature maps at similar scales to exploit rich context information. The narrowed semantic gap then can generate features which can be more efficiently utilized by the decoder. Further, we integrate Convolutional Network Next(ConvNext) block into our encoder module to enhance feature extraction capabilities. Experimental simulations show that our FCoDT-Net excels in both 3D and 2D applications. In 3D, FCoDT-Net achieves exceptional results on the lung dataset, with a Dice score of 0.9250 for airway segmentation. In 2D, FCoDT-Net demonstrates state-of-the-art performance on both the Gland Segmentation (Glas) and Multi-Organ Nuclei Segmentation and Classification (MoNuSeg) datasets.

关键词： Solid modeling Three-dimensional displays Accuracy Semantic segmentation Semantics Glands Transformers Decoding Speech processing Medical diagnostic imaging

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Denoising of COVID-19 CT and chest x-ray images using deep learning techniques for various noises using single image

Denoising of COVID-19 CT and chest X-ray images using deep l...

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signal processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International conference on

作者： G Ashwini T. Ramashri Department of Electronics and Communication Engineering SV University Tirupati India

The onset of COVID-19 pandemic had a great impact on the health, economy and livelihood of so many lives around the world. Early identification of positive cases and isolation followed by treatment is very crucial in order to receive prompt treatment and prevent the virus from spreading further. Chest x-ray (CxR) and Computed Tomography (CT) are widespread and cost-effective medical imaging radiographic tools which are presently used for diagnosing the covid-19 quickly. A crucial step towards establishing a quick COVID-19 pre-diagnosis and reducing the workload on medical professionals is the use of deep learning algorithms to identify positive CxR and CT pictures of infected individuals. However, the CxR images have complicated edge structures and rich texture details that are sensitive to noise, which can interfere with the machines and doctors diagnosis. This paper presents two state-of-art denoising techniques, Noise2Noise(N2N) and Noise2Void(N2V),to eliminate the noises that were added to COVID-19 chest x-ray scan image and CT medical image modalities by additive Gaussian noise, Speckle noise, and salt-and-pepper noise. These two techniques do not require a pair of noisy and clean photos; instead, they denoise a single noisy image. Based on the study, Noise2Void performs well in removing Gaussian, Speckle, and salt-and-pepper noise from CxR image modalities. Similarly, Noise2Noise performance is good to remove only Gaussian noise in CT images. In the case of Speckle and salt and pepper noise in CT images. Noise2void gives better quality images with better PSNR and SSIM. The results are measured quantitatively and qualitatively using Peak signal to Noise Ratio (PSNR) and Structural Similarity Index Measure(SSIM). In this paper, we experiment two learning based methods to de-noise images with high noise. The proposed method is beneficial for applications involving images that are highly prone to noise.

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Gop Size Effect on VVC Encoder Performance with MRF Varation

Gop Size Effect on VVC Encoder Performance with MRF Varation

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

作者： Rana Jassem Taheni Damak Mohamed Ali Ben Ayed Nouri Masmoudi Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah Iraq New Technologies and Telecommunications Systems (NTS’Com) Sfax University Tunisia Laboratory of electronics and technology’s information (LETI) Sfax University Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

The exponential growth of multimedia content and applications underscores the ongoing challenge of developing advanced video coding standards that not only enhance compression rates but also incorporate essential features while addressing increasing complexity in computational processing. Among these standards, the Versatile Video Coding (VVC) standard stands out for its superior efficiency compared to its predecessors. As one of the key parameters in video compression algorithms, the size of Group of Pictures (GOP) determines the frames interdependency in the encoded video. The GOP size significantly influences the performance of a Video Coding Standard (VVC) encoder, particularly in conjunction with Multiple Reference Frame (MRF) variation. Adjusting the GOP size can have a profound impact on compression efficiency and visual quality for video coding. In this study, we investigate the effect of varying MRF configurations and GOP sizes on the performance of VVC encoder. Furthermore, we advance the notion that achieving diverse MRF configurations is feasible through strategic adjustments to the number of reference frame within each reference list. In this paper, we present statistical results showing the impact of different Quantization Parameters (QPs) and GOP size on encoder performance for each configuration. Based on our results, varying GOP size with MRF configurations has a significant positive impact on encoder performance as measured by bitrate and execution time for several values of QP without affecting the quality of the reconstructed video and the resulted bitrate.

关键词： Video coding Radio frequency Visualization Quantization (signal) Bit rate Video compression Streaming media

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