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检索条件"任意字段=International Symposium on Image and Signal Processing and Analysis"

共 54084 条记录，以下是141-150 订阅

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AGIAA-2K: A Fine-grained Dataset for Aesthetic and Alignment Evaluation of AI-Generated images

AGIAA-2K: A Fine-grained Dataset for Aesthetic and Alignment...

引用

international Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Bo Hu Nanxiang Li Lihuo He Wen Lu Leida Li Xinbo Gao Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Chongqing China School of Electronic Engineering Xidian University Xi’an China School of Artificial Intelligence Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the advancement of AI-generated content technologies, AI-generated images (AGIs) have become increasingly influential in artistic creation and visual communication. However, the aesthetic quality of AGIs varies significantly due to technical limitations and the influence of user input, underscoring the urgent need for systematic aesthetic evaluation of AGIs. In addition, it is difficult to ensure the consistency of text-to-image, which compresses the application space of AGIs. To address these issues, a fine-grained dataset for Aesthetic and Alignment evaluation of AGIs (AGIAA-2K) is presented. This dataset contains 2,064 images generated using 172 well-designed prompts across six different AGI models, with each image annotated based on the subjective experiment. Then, the rationality of the dataset is verified by data analysis. Finally, the performances of the existing algorithms are evaluated in terms of image aesthetic assessment and text-to-image alignment of AGIs. The results demonstrate that these algorithms cannot effectively evaluate these two aspects. The AGIAA-2K is available at https://***/BoHu90/AGIAA-2K.

关键词： Measurement Technological innovation Systematics image coding Data analysis Visual communication Text to image signal processing algorithms signal processing Speech processing

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Prompt Fusion and Aspect-Oriented Filtration for Aspect-Based Multimodal Sentiment analysis

Prompt Fusion and Aspect-Oriented Filtration for Aspect-Base...

引用

international Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jiachang Sun Xiuhong Li Fuxian Zhu School of Computer Science and Technology XinJiang University Urumqi China Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China School of Economics and Management XinJiang University Urumqi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Currently, aspect-based multimodal sentiment analysis (ABMSA) remains a highly hot research field, aiming to leverage various modalities such as images and text to determine the sentiment orientation of viewpoint entities. Although deep learning methods have made significant progress in this field, some challenges still exist: image information unrelated to the viewpoint entity can introduce noise, insufficient interaction during modal fusion. To solve these problems, we propose a novel ABMSA model with prompt fusion and noise filtration. Specifically, we introduced an image Noise Filtering (INF) module that can filter the extracted image features to reduce the noise introduced by the irrelevant between viewpoint entity and the image. Additionally, we implemented Multimodal Prompt Fusion (MPF) module to enhance the interaction between modalities, addressing the issue of minimal effective features related to viewpoint entity during the fusion stage of text and image features. Experimental results show that the model outperforms the baseline model on two public datasets, Twitter-2015 and Twitter-2017, demonstrating that our proposed approach effectively enhances the accuracy of aspect-based multimodal sentiment analysis tasks.

关键词： Deep learning Sentiment analysis Analytical models Accuracy Filtration Noise Feature extraction Information filters Acoustics Speech processing

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LLDB: Efficient Low-Light image Enhancement with Difffusion Bridge

LLDB: Efficient Low-Light Image Enhancement with Difffusion ...

引用

international Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Junlong Ma Conghan Yue Zhengwei Peng Dongyu Zhang Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper investigates a low-light image enhancement method based on the diffusion bridge framework. Currently, low-light image enhancement tasks still face challenges in noise reduction and detail restoration, and existing diffusion model methods are time-consuming and have unstable diffusion processes. We conducted an in-depth study of diffusion bridge theory, integrating the advantages of the end-to-end paradigm of diffusion bridge theory and employing a nonlinear activation network to further enhance the performance of low-light image enhancement tasks. Additionally, we used a Gamma correction module for fine-tuning low-light images, significantly improving performance with almost no extra computational cost. Experiments show that Low-Light image enhancement with Difffusion Bridge (LLDB) far surpasses other methods on LOLv1 and LOLv2 datasets. Code is available at https://***/M-Chase/LLDB

关键词： Bridges Computational modeling Noise reduction Diffusion processes Speech enhancement signal processing Diffusion models image restoration image enhancement Faces

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MLSwinTNet: A Multi-Level Feature Interaction Network for Low-Light image Enhancement

MLSwinTNet: A Multi-Level Feature Interaction Network for Lo...

引用

international Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Mingyang Sun Xinxin Wang Ru Yi College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Low-Light image Enhancement (LLIE) is crucial for improving image quality and visual analysis. This study proposes MLSwinTNet, an LLIE network based on the Swin Transformer. The core MLSwinT module adopts a UNet-like design, integrating a dual-branch feature extraction module and a multi-level feature interaction module. The former combines CNN and Swin Transformer to efficiently capture both local and global features, while the latter optimizes detail and color restoration by adjusting and fusing feature levels. Experimental results demonstrate that MLSwinTNet outperforms state-of-the-art methods on standard datasets, achieving a PSNR gain of 27.33 dB and an SSIM score of over 0.96, providing a more competitive solution for LLIE.

关键词： Measurement Visualization image color analysis Transformer cores Speech enhancement Feature extraction Transformers image restoration Standards image enhancement

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DEFormer: DCT-driven Enhancement Transformer for Low-light image and Dark Vision

DEFormer: DCT-driven Enhancement Transformer for Low-light I...

引用

international Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Xiangchen Yin Zhenda Yu Xin Gao Xiao Sun Institute of Artificial Intelligence Hefei Comprehensive National Science Center University of Science and Technology of China Hefei China Hefei Comprehensive National Science Center Anhui University Institute of Artificial Intelligence Hefei China School of Vehicle and Mobility Tsinghua University Beijing China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Low-light image enhancement restores the colors and details of a single image and improves high-level visual tasks. However, restoring the lost details in the dark area is still a challenge relying only on the RGB domain. In this paper, we delve into frequency as a new clue into the model and propose a DCT-driven enhancement transformer (DEFormer) framework. First, we propose a learnable frequency branch (LFB) for frequency enhancement contains DCT processing and curvature-based frequency enhancement (CFE) to represent frequency features. Additionally, we propose a cross domain fusion (CDF) to reduce the differences between the RGB domain and the frequency domain. Our DEFormer has achieved superior results on the LOL and MIT-Adobe FiveK datasets, improving the dark detection performance.

关键词： Deformable models Visualization image color analysis Frequency-domain analysis Speech enhancement signal processing Transformers image restoration Discrete cosine transforms image enhancement

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A New Color Manipulation Method and Dehazing for Enhancement of Underwater images

A New Color Manipulation Method and Dehazing for Enhancement...

引用

international Conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： M. Kanagavel V. Thanikaiselvan School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

In this article, a new underwater image enhancement method is proposed based on image classification and blending of color manipulation and dehazing. First, the underwater images are classified majorly into three types: water haze image, blue toned image and green toned image, using the mean pixel intensity of color channels in RGB space. Further, depending upon the maximum pixel intensity level of blue and green channels, the blue toned and green toned images are classified into two subtypes: blue-green and green-blue toned images. The water haze images are enhanced using the shades of grey method followed by dehazing by Dark Channel Prior (DCP). The green and blue toned images are enhanced by color mixing method followed by DCP. The blue-green and green-blue images are improved by a new proposed color manipulation method followed by DCP. The experimental results show that the proposed approach outperforms the state-of-the-art qualitatively and quantitatively in terms of Underwater image Quality Metrics (UIQM).

关键词： Wireless communication Measurement image quality image color analysis Absorption Scattering signal processing Attenuation image enhancement image classification

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Deep-Relative-Trust-Based Diffusion for Decentralized Deep Learning

Deep-Relative-Trust-Based Diffusion for Decentralized Deep L...

引用

international Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Muyun Li Aaron Fainman Stefan Vlaski Department of Electrical and Electronic Engineering Imperial College London

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Decentralized learning strategies allow a collection of agents to learn efficiently from local data sets without the need for central aggregation or orchestration. Current decentralized learning paradigms typically rely on an averaging mechanism to encourage agreement in the parameter space. We argue that in the context of deep neural networks, which are often over-parameterized, encouraging consensus of the neural network outputs, as opposed to their parameters can be more appropriate. This motivates the development of a new decentralized learning algorithm, termed DRT diffusion, based on deep relative trust (DRT), a recently introduced similarity measure for neural networks. We provide convergence analysis for the proposed strategy, and numerically establish its benefit to generalization, especially with sparse topologies, in an image classification task.

关键词： Deep learning Network topology signal processing algorithms Artificial neural networks signal processing Topology Classification algorithms Speech processing image classification Convergence

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PAFedMIS: Personalized Asynchronous Federated Learning for Medical image Segmentation

PAFedMIS: Personalized Asynchronous Federated Learning for M...

引用

international Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yi Li Yue Hua Xin Zheng Yanqing Guo Bo Wang Dalian University of Technology China Griffith University Australia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

As privacy protection gains momentum, federated learning has emerged as a cutting-edge approach in medical image analysis. However, the intricacies of medical image segmentation task have led to a dearth of research in this domain, with existing studies falling short in tackling two pivotal challenges: The traditional model with the uniform global model underperforms for certain clients due to the heterogeneity and non-Independent Identically Distributed(non-IID) data across medical institutions. And the communication between the server and clients often incurs significant time costs. This paper introduces a novel Personalized Asynchronous Federated learning for Medical image Segmentation model, dubbed PAFedMIS, to mitigate the negative impact of the heterogeneous data and fully utilized the waiting time, in medical image segmentation. Comprehensive experiments on ISIC2018 demonstrate the enhanced model accuracy and training efficiency of PAFedMIS.

关键词： Training image segmentation Accuracy Federated learning signal processing Data models Servers Speech processing Protection Biomedical imaging

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Application of Deconvolution processing in Passive Localization Technology for Circular Array

Application of Deconvolution Processing in Passive Localizat...

引用

international symposium on Underwater Technology

作者： Bo-Hsien Wu Systems and Naval Mechatronic Engineering National Cheng Kung University Tainan Taiwan

ISBN: (数字)9798331534189

ISBN: (纸本)9798331534196

In the present study, a robust passive localization technology, based on a uniform circular array and combining beamforming with deconvolution processing, is developed. The goal is to be applicable in low SNR and low snapshot conditions and to be applied to cylindrical sonar systems. The study proposes two deconvolution processing methods and compares them with traditional methods: delay-and-sum beamforming (DSB) and multiple signal classification (MUSIC). The first method uses the Richardson-Lucy (RL) algorithm, a common image enhancement technique in astronomy. The second method use the deconvolution approaches for acoustic source mapping (DAMAS). Both static and dynamic scenarios are used in the simulation. In the static scenario, the array performance of each method is analyzed under different conditions of frequency, signal-to-noise ratio (SNR), and snapshot. The resolution (main lobe width), main-to-sidelobe ratio (MSR), directivity factor (DF), and localization accuracy are used as quantitative parameters for analysis. In the dynamic scenario, multi-target motion and the Doppler effect are considered to verify the feasibility of the proposed methods. The results show that, compared to the free-space array configuration, the rigid cylinder array configuration offers better MSR and DF. Additionally, the two deconvolution methods proposed in this study indeed exhibit better resolution and localization accuracy than traditional methods.

关键词： Location awareness Analytical models Deconvolution Accuracy Array signal processing Sonar applications Numerical models Spatial resolution Underwater technology signal to noise ratio

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Forensics analysis of Residual Noise Texture in digital images for Detection of Deepfake

Forensics Analysis of Residual Noise Texture in digital Imag...

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international Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Arthur Méreur Antoine Mallet Rémi Cogranne Minoru Kuribayashi Troyes University of Technology France Center for Data-driven Science and Artificial Intelligence Tohoku University Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper proposes an original approach for the automatic detection of AI-generated images, using features derived from noise residuals artefacts. Contrary to most current research that leverages sophisticated deep learning models to further improve performance, this study highlights the distinct noise residual characteristics in deepfakes, facilitating the identification of AI-generative images. Our findings highlight some limitations of image models, which can be used for forensic analysis and for future AI-based text-to-image generative models. Broad numerical results on a large and diverse dataset show the interest of the identified features as well as the relevance of the present method.

关键词： Deep learning Deepfakes Analytical models Forensics Digital images Noise Text to image Feature extraction Numerical models Speech processing

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