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Stochastic Vector Approximate Message Passing with applications to Phase Retrieval

Stochastic Vector Approximate Message Passing with Applicati...

引用

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

作者： Hajime Ueda Shun Katakami Masato Okada Graduate School of Frontier Sciences The University of Tokyo Kashiwa Chiba Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Phase retrieval refers to the problem of recovering a high-dimensional vector ${\mathbf{x}} \in {\mathbb{C}^N}$ from the magnitude of its linear transform z = Ax, observed through a noisy channel. To improve the ill-posed nature of the inverse problem, it is a common practice to observe the magnitude of linear measurements z (1) = A (1) x,…, z (L) = A (L) x using multiple sensing matrices A (1) ,…, A (L) , with ptychographic imaging being a remarkable example of such strategies. Inspired by existing algorithms for ptychographic reconstruction, we introduce stochasticity to Vector Approximate Message Passing (VAMP), a computationally efficient algorithm applicable to a wide range of Bayesian inverse problems. By testing our approach in the setup of phase retrieval, we show the superior convergence speed of the proposed algorithm.

关键词： Inverse problems Message passing signal processing algorithms Imaging Approximation algorithms Vectors Bayes methods Probes image reconstruction Convergence

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Enhancing Boundary-Handling Strategies for Convolutional Sparse Representation Model with 46 × 46 Convolution-Multiplication Properties

Enhancing Boundary-Handling Strategies for Convolutional Spa...

引用

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

作者： Yuto Tsukiashi Yoshimitsu Kuroki National Institute of Technology Kurume College Fukuoka Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Convolutional sparse representation (CSR) extends the standard form by incorporating convolutional operations and has achieved notable success in image processing applications, offering a shift-invariant representation model. Recent studies typically redesign the initial model by exploiting the convolution-multiplication property of the discrete Fourier transform to solve the involved convex optimizations efficiently, which imposes periodic boundary handling and risks introducing boundary artifacts. This paper generalizes this approach by leveraging 46 × 46 convolution-multiplication properties while requiring only extra element-wise operations, enabling model designs that combine periodic, antiperiodic, and 40 symmetric boundary handlings for each dimension and across convolutions. These newly accepted models demonstrated better representation performance than the conventional model in our experiments, indicating the benefits of selecting appropriate boundary handlings.

关键词： Convolution Sparse approximation Computational modeling image processing Memory management Transforms Linear programming Convex functions Speech processing Standards

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Class Relevance Learning for Out-of-Distribution Detection

Class Relevance Learning for Out-of-Distribution Detection

引用

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

作者： Liguang Zhou Butian xiong Tin Lun Lam Yangsheng xu School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

image classification plays a pivotal role across diverse robotic applications, yet challenges persist when models are deployed in real-world scenarios. These models often fail to detect out-of-distribution (OOD) samples, classes not included in their training. This makes OOD detection a significant challenge for safe and effective real-world use. While existing techniques, like max logits, aim to leverage logits for OOD identification, they often disregard the intricate interclass relationships that underlie effective detection. This paper presents an innovative class relevance learning (CRL) method tailored for OOD detection. Our method establishes a comprehensive class relevance learning framework, strategically harnessing interclass relationships within the OOD pipeline. This framework significantly augments OOD detection capabilities. Extensive experimentation on diverse datasets, encompassing generic image classification datasets (Near OOD and Far OOD datasets), demonstrates the superiority of our method over state-of-the-art alternatives for OOD detection. The code is available on GitHub at: CRL.

关键词： Training Codes Pipelines Estimation signal processing Probability Speech processing Robots image classification Software development management

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PsSR: Hybrid Path Selection Mechanism for Efficient image Super-Resolution

PsSR: Hybrid Path Selection Mechanism for Efficient Image Su...

引用

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

作者： Cheng Ding ZhongQiu Zhao Yang Zhao School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In practical applications, large images are processed in patches, many of which are simple and smooth, making them suitable for lighter network processing. This paper proposes a hybrid path selection mechanism that enables the dynamic routing of patches through the network, achieving a balance between accuracy and computational efficiency. The proposed method develops a dual-branch architecture, in which the two branches keep the same structural design, but differ in the number of channels. An efficient classification module is integrated for directing patches to the appropriate branches within the model. In addition, within each branch, we introduce an early exit strategy to further reduce the calculation cost. Subsequently, we enhance the restoration capabilities of the lightweight branch through channel and spatial feature distillation. Experimental results demonstrate that our Hybrid Path Selection based Super-Resolution (PsSR) mechanism effectively reduces computational costs while preserving the integrity of the backbone performance.

关键词： Costs Computational modeling Superresolution Computer architecture Routing Computational efficiency image restoration Speech processing Spatial resolution signal resolution

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Let There Be Light: Robust Lensless Imaging Under External Illumination With Deep Learning

Let There Be Light: Robust Lensless Imaging Under External I...

引用

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

作者： Eric Bezzam Stefan Peters Martin Vetterli Audiovisual Communications Laboratory École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Lensless cameras relax the design constraints of traditional cameras by shifting image formation from analog optics to digital post-processing. While new camera designs and applications can be enabled, lensless imaging is very sensitive to unwanted interference (other sources, noise, etc.). In this work, we address a prevalent noise source that has not been studied for lensless imaging: external illumination e.g. from ambient and direct lighting. Being robust to a variety of lighting conditions would increase the practicality and adoption of lensless imaging. To this end, we propose multiple recovery approaches that account for external illumination by incorporating its estimate into the image recovery process. At the core is a physics-based reconstruction that combines learnable image recovery and denoisers, all of whose parameters are trained using experimentally gathered data. Compared to standard reconstruction methods, our approach yields significant qualitative and quantitative improvements. We open-source our implementations and a 25K dataset of measurements under multiple lighting conditions.

关键词： Source separation Noise Lighting Interference Reconstruction algorithms Cameras Optics Speech processing image reconstruction Standards

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Spatial-Temporal Reconstruction Error for AIGC-based Forgery image Detection

Spatial-Temporal Reconstruction Error for AIGC-based Forgery...

引用

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

作者： Chengji Shen Zhenjiang Liu Kaixuan Chen Jie Lei Mingli Song Zunlei Feng State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Idlefish Network Technology Alibaba Group College of Computer Science and Technology Zhejiang University of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The remarkable success of AI-Generated Content (AIGC), especially diffusion image generation models, brings about unprecedented creative applications, but also creates fertile ground for malicious counterfeiting and crime. A highly effective family of forgery image detection methods based on diffusion reconstruction error has emerged, as images generated by diffusion are more easily reconstructed by any diffusion model. However, we find that existing methods only use reconstruction error from a single time step, failing to fully leverage the entire reconstruction process. To this end, we propose to comprehensively consider every single time step to form the Temporal Reconstruction Error (TRE) that offers a richer feature representation. Furthermore, we design temporal aggregation and spatial focusing modules from two dimensions respectively to more effectively extract discriminative information from the TRE feature. Finally, we validate the proposed method on two popular datasets, and experimental results demonstrate that the proposed approach achieves state-of-the-art performance.

关键词： Deepfakes image synthesis Focusing signal processing Feature extraction Diffusion models Forgery Data mining Speech processing image reconstruction

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An Efficient Automatic Car Parking Slot Allocation System

An Efficient Automatic Car Parking Slot Allocation System

引用

International conference on signal processing and Communication (ICSC)

作者： N Srivani Prasanth A K Srija Chinthala Ramesh Jayashree M. Oli Department of Electronics and Communication Engineering Amrita School of Engineering Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331511890

ISBN: (纸本)9798331511906

This work presents an automated car parking allocation system designed to optimize and enhance the efficiency of parking management in urban environments. The system utilizes an Arduino board and a camera to detect vehicle number plates, store pertinent information, and allocate available parking spaces effectively. By employing image processing techniques and optical character recognition (OCR), the system processes images captured by the camera to extract and display number plate data. The traditional parking systems have common challenges, such as wrong allocation of parking slots, which leads to congestion and inconvenience to users. The proposed innovative automated car parking allocation system aims to resolve common challenges in traditional parking systems by providing a more organized and automated approach. This system has been tested in various scenarios, demonstrating its potential to significantly improve operational efficiency and user satisfaction in real-time applications.

关键词： Space vehicles image resolution Urban areas Automated parking Cameras Real-time systems Resource management Data mining License plate recognition signal resolution

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A Parametric Non-Negative Coupled Canonical Polyadic Decomposition Algorithm for Hyperspectral Super-Resolution

A Parametric Non-Negative Coupled Canonical Polyadic Decompo...

引用

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

作者： xi-Yuan Liu xiao-Feng Gong Lei Wang Wei Feng Qiu-Hua Lin School of Information and Communication Engineering Dalian University of Technology China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, coupled tensor decomposition has been widely used in data fusion of a hyperspectral image (HSI) and a multispectral image (MSI) for hyperspectral super-resolution (HSR). However, exsiting works often ignore the inherent non-negative (NN) property of the image data, or impose the NN constraint via hard-thresholding which may interfere with the optimization procedure and cause the method to be sub-optimal. As such, we propose a novel NN coupled canonical polyadic decomposition (NN-C-CPD) algorithm, which makes use of the parametric method and nonlinear least squares (NLS) framework to impose the NN constraint into the C-CPD computation. More exactly, the NN constraint is converted into the squared relationship between the NN entries of the factor matrices and a set of latent parameters. Based on the chain rule for deriving the derivatives, the key entities such as gradient and Jacobian with regards to the latent parameters can be derived, thus the NN constraint is naturally integrated without interfering with the optimization procedure. Experimental results are provided to demonstrate the performance of the proposed NN-C-CPD algorithm in HSR applications.

关键词： Jacobian matrices Tensors Superresolution Noise signal processing algorithms Matrix decomposition Matrix converters Speech processing Optimization Hyperspectral imaging

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Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Size

Boosting the Transferability of Adversarial Examples via Loc...

引用

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

作者： Junlin Liu Chenyu Zhang xinchen Lyu National Engineering Research Center for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Adversarial examples are one critical security threat to various visual applications, where injected human-imperceptible perturbations confuse the output. Generating transferable adversarial examples in the black-box setting is crucial but challenging in practice. Existing input-diversity-based methods adopt different image transformations, but may be inefficient due to insufficient input diversity and an identical perturbation step size. Motivated by the fact that different image regions have distinctive weights in classification, this paper proposes a black-box adversarial generative framework by jointly designing enhanced input diversity and adaptive step sizes. We design local mixup to randomly mix a group of transformed adversarial images, strengthening the input diversity. For precise adversarial generation, we project the perturbation to relax the boundary constraint. Moreover, the step sizes of different regions can be dynamically adjusted by integrating a second-order momentum. Extensive experiments on imageNet validate that our framework can achieve superior transferability compared to state-of-the-art baselines. Code is available at https://***/ZeroWalker10/IDAA.

关键词： image transformation Visualization Perturbation methods Diversity reception Closed box Transforms signal processing Security Speech processing Optimization

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Assessing Robustness of Multi-Modal Large Language Models in image Classification through Hierarchical WordNet-Based Evaluation

Assessing Robustness of Multi-Modal Large Language Models in...

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

作者： Chang Liu Hai Chen Boxiang Wang Shibao Zheng Shanghai Jiao Tong University Shanghai China Anhui Unversity Hefei China Nanyang Technological University Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The advancement of multi-modal large language models (MLLMs) has significantly enhanced their capability to process and understand diverse data types, integrating text, images, and other modalities. Despite their impressive performance, evaluating the robustness of these models remains challenging due to the difficulty of aligning their text-based responses with image classification labels. Traditional approaches rely on CLIP scores or other large language models as judges, but these methods lack scientific rigor and fail to capture robustness across different semantic levels. In this paper, we propose a novel evaluation metric that systematically assesses the robustness of MLLMs in image classification using WordNet’s hierarchical structure. Specifically, we parse the text descriptions generated by MLLMs to extract all nouns, then calculate their distances to the groundtruth label in WordNet as a similarity metric. The minimum distance among all nouns is used as the similarity score between the text description and the label. By using WordNet, we can also evaluate classification performance at different semantic levels. Through extensive experiments, we demonstrate that our WordNet-based evaluation metric offers a deeper understanding of MLLMs’ robustness, paving the way for more resilient and reliable models in real-world applications.

关键词： Measurement Visualization Large language models Semantics Lexicon Linguistics signal processing Robustness Speech processing image classification

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