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检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

共 20445 条记录，以下是1-10 订阅

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1st ieee international conference on signal-image technology and internet-based systems, SITIS 2005

1st IEEE International Conference on Signal-Image Technology...

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1st ieee international conference on signal-image technology and internet-based systems, SITIS 2005

The proceedings contain 40 papers. The topics discussed include: automatic text regions location in video frames;automated detection and classification of clusters of microcalcifications in mammograms;panoramic face mosaicing and recognition using multi-cameras;facial identification problem: a tracking based approach;comparison of textural parameters for color textures analysis;characterization of the anisotropy in color textures;textures in images classification using a multifractal approach;a new wavelet based multi-resolution texture segmentation scheme of remotely sensed images for vegetation extraction;distant horizontal ground observation: atmospheric perturbation simulation and image restoration;neural network nonlinear factor analysis of high dimensional binary signals;and wavelet-based feature extraction for musical genre classification using support vector machines.

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TDMF: Text-Guided Denoising and Interactive Medical image Fusion

TDMF: Text-Guided Denoising and Interactive Medical Image Fu...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Dong, Aimei Xu, Jingyuan Wang, Long Lv, Guohua Zhao, Guixin Cheng, Jinyong Qilu University of Technology Shandong Academy of Sciences Jinan China Faculty of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798350368741

Multimodal image fusion aims to merge features from different modalities to create a comprehensively representative image. However, existing medical image fusion methods often struggle to handle noise generated during image acquisition, significantly diminishing their impact on visual quality. To address these challenges, we propose a semantically text-guided medical image fusion model, named TDMF. Specifically, TDMF guides classical image fusion through textual semantics and effectively coordinates the resolution of degradation and interaction issues during the fusion process. By integrating text encoders and interactive fusion modules, TDMF establishes a unified framework for denoising and interactive fusion of medical images. Extensive experiments have demonstrated that our proposed text-guided image fusion strategy offers significant advantages over state-of-the-art methods in medical image fusion performance. © 2025 ieee.

关键词： denoising Medical image fusion text-guided fusion

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Explaining Representations in Correlation-based Deep Multiview Representation Learning

Explaining Representations in Correlation-based Deep Multivi...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Kuschel, Maurice Alkhatib, Amr Hasija, Tanuj Boström, Henrik Signal and System Theory Group Paderborn University Paderborn Germany Division of Software and Computer Systems KTH Royal Institue of Technology Stockholm Sweden

ISBN: (纸本)9798350368741

Multiview representation learning techniques based on deep correlation maximization have become increasingly popular for learning meaningful and compact representations from multiview data. Even though their performance is state-of-the-art in many interpretability-critical fields, their black-box behavior poses a problem and restricts their usability. To overcome this restriction, we propose XDCCA (eXplanations for Deep Canonical Correlation Analysis), an explanation strategy using characteristic rules in combination with SHAP that exploits the inherent structure of latent spaces created by correlation maximization techniques. We demonstrate how XDCCA allows for interpreting learned representations and their correlation using real medical time series and synthetic image data. © 2025 ieee.

关键词： CEGA Deep Canonical Correlation Analysis Explainable AI Multiview Representation Learning SHAP

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Medical image Authentication and Self-Recovery Using Fragile Watermarking in the Frequency Domain 6

Medical Image Authentication and Self-Recovery Using Fragile...

引用

6th ieee international conference on image Processing, Applications and systems, IPAS 2025

作者： Bouarroudj, Riadh Harrou, Fouzi Zerrouki, Nabil Souami, Feryel Bellala, Fatma Zohra Sun, Ying University of Sciences and Technology Houari Boumedienne Department of Computer Science Algiers Algeria King Abdullah University of Science and Technology CEMSE Division Thuwal Saudi Arabia Center for Development of Advanced Technology DI2M Laboratory Algiers Algeria University of Algiers Benyoucef Benkhedda Department of Computer Science Algiers Algeria

ISBN: (纸本)9798331506520

The rapid growth in digital image sharing, driven by advancements in internet and communication technologies, has raised concerns about image integrity, especially in sensitive fields like healthcare. This paper presents a fully blind fragile watermarking technique for authenticating and self-recovering color and grayscale medical images. The approach applies the Discrete Wavelet Transform (DWT) to the cover image, and the resulting subbands are divided into 3×3 blocks. One authentication and four recovery watermarks are then embedded into the least significant bits (LSBs) of each block. During the extraction phase, if tampering is detected, the model accurately localizes the altered areas, and a three-level recovery process, including a new inpainting technique, is used to recover the original image. Results based on two publically available datasets demonstrate that this method delivers high-quality watermarked images, achieves optimal watermark extraction accuracy, and maintains high sensitivity to various attacks. Additionally, it provides precise tamper localization and delivers high-quality recovered images, even with tampering rates as high as 60%. © 2025 ieee.

关键词： Frequency domain analysis

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Diffusion Model based image Reconstruction in Lensless Imaging

Diffusion Model Based Image Reconstruction in Lensless Imagi...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Verma, Ashish Boominathan, Vivek Veeraraghavan, Ashok Seelamantula, Chandra Sekhar Department of Electrical Engineering Indian Institute of Science Bengaluru560 012 India Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States

ISBN: (纸本)9798350368741

Lensless imaging systems eliminate the need for lenses by employing an encoding element to multiplex incident light signals, which are then captured directly onto a bare camera sensor. They present a promising alternative to traditional lens-based imaging systems by offering significant advantages in terms of compactness, versatility, and cost. Due to the multiplexed nature of measurements, image reconstruction takes place computationally. However, existing techniques for image reconstruction in lensless imaging fall short of the image quality offered by traditional lens-based imaging. In this work, we consider the application of diffusion models, a class of deep generative models, for image reconstruction in a lensless imaging modality. These models currently achieve state-of-the-art performance in image generation. Specifically, we focus on the PhlatCam lensless system, which consists of a coded phase mask as the encoding element placed close to the camera sensor. We use a ControlNet based diffusion model to improve the perceptual quality of image reconstruction. The performance is measured in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and learned perceptual image patch similarity (LPIPS). The proposed method improves the performance in these metrics for synthetic measurements. For real measurements, the improvement in image quality comes at the expense of a small bias in color, which is attributed to the generative nature of the diffusion prior itself. © 2025 ieee.

关键词： Computational imaging diffusion model generative model lensless imaging stable diffusion

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River-GEM: Generating and Enhancing Muddy Water images

River-GEM: Generating and Enhancing Muddy Water Images

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Pramanick, Alik Satwik, Chivukula Sairam Kumar, Sonal Daydar, Akshay Sur, Arijit Indian Institute of Technology Guwahati India

ISBN: (纸本)9798350368741

Underwater image enhancement is crucial for marine engineering and aquatic robotics. However, most recent methods have focused on ocean environments, where they trained and tested on oceanic images. As a result, these methods are less effective in river water, where relatively blurry images are produced due to extensive muddy environments. In river water-based image restoration, we have observed two main limitations: (1) the lack of datasets that accurately represent the muddy water conditions found in river environments and (2) the limited effectiveness of current enhancement models in dealing with the unique challenges of muddy water images. To address these limitations, we introduce the "River-GEM" framework, which includes (1) a dataset of highly degraded muddy water images generated using a principle component analysis-based fusion technique and (2) a novel enhancement model utilizing the depth-map guidance to learn complex contextual details of muddy water images. The proposed model captures the intricate details of the objects in degraded images by analyzing feature correlation at multiple scales. Our model shows improvements of 3.42% in SSIM and 5.04% in PSNR over existing methods on the muddy-UIEB dataset. This dataset and enhancement network marks a significant step forward in this area of research. The dataset and code will be available at: https://***/Alik033/River-GEM. © 2025 ieee.

关键词： image enhancement Muddy image generation principle component analysis transformer

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An image encryption algorithm based on non-degenerate chaos and dynamic Bi-directional permutation 16

An image encryption algorithm based on non-degenerate chaos ...

引用

16th international conference on signal Processing systems, ICSPS 2024

作者： Liao, Yunlong Xu, Qingyang Zhang, Qiyuan Xing, Zheng Yuan, Xiaochen Faculty of Applied Sciences Macao Polytechnic University China

ISBN: (纸本)9781510689251

In the wake of the accelerated advancement of internet of Things(IoT) technology, a significant volume of multimedia information is emerging as the dominant component of IoT applications. However, this information also led to big security risks. Therefore, a secure and reliable image encryption scheme based on non-degenerate hyper chaos and dynamic bidirectional permutation is proposed for the characteristic of digital images which the primary information carrier in the IoT communication environment. The scheme uses non-degenerate discrete chaos to generate pseudo-random sequences and applies them to the encryption process involving image confusion, dynamic permutation, and dynamic diffusion. Through theoretical analysis and experimental verification, the algorithm exhibits high sensitivity to both the plaintext images and the ciphertext images. Concurrently, it can effectively resist common noise attacks and cryptographic attacks. In conclusion, the proposed scheme offers superior encryption capabilities and stands as a secure solution, compatible with a diverse range of IoT environments. © 2025 SPIE.

关键词： Chaos theory

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A Grouping Strategy-based Progressive Fusion Network for Hyperspectral image Super-Resolution

A Grouping Strategy-Based Progressive Fusion Network for Hyp...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Lv, Guohua Zhang, Baodong Gao, Yongbiao Zhao, Guixin Wang, Guotao Wang, Juncan Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China Ministry of Education China Beihang University Beijing China

ISBN: (纸本)9798350368741

Hyperspectral super-resolution involves combining low-resolution hyperspectral images with high-resolution multispectral images to produce a high-resolution hyperspectral image. Recently, although many methods for hyperspectral image super-resolution have been proposed, they often fail to fully utilize the high similarity among adjacent bands to enhance fusion performance. Therefore, we propose a grouping strategy-based progressive fusion network (GPFNet) for hyperspectral super-resolution. The core of GPFNet is the grouping strategy fusion block (GPF block), in which grouping-based spatial-spectral information fusion and spatial information refinement are performed. We design the spatial-spectral information fusion module (SSIFM) based on grouped convolutions to capture the feature differences from adjacent bands. To refine spatial details, we develop the spatial information enhancement module (SpaEM), which leverages the hierarchical features extracted by the multi-scale feature extraction module (MIEM). Additionally, a progressive fusion strategy, which involves using multiple upsampled hyperspectral images and downsampled multispectral images, further preserves spectral integrity and spatial details. Extensive experiments show that GPFNet outperforms state-of-the-art methods both qualitatively and quantitatively. © 2025 ieee.

关键词： group convolution Hyperspectral image image fusion super-resolution

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RWKVMatch: Vision RWKV-based Multi-scale Feature Matching Network for Unsupervised Deformable Medical image Registration

RWKVMatch: Vision RWKV-based Multi-scale Feature Matching Ne...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： He, Zixuan Tang, Jing Zhao, Zitong Gong, Zeyu School of Future Technology Huazhong University of Science and Technology Hubei Wuhan China School of Mechanical Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9798350368741

Medical image registration is essential for integrating information from diverse imaging modalities for clinical diagnosis and treatment planning. Despite significant advancements, achieving efficient and precise deformable image registration remains a formidable challenge. In this study, we propose a novel medical image registration model, RWKVMatch, which employs global attention and cross-fusion mechanism based on the Vision-RWKV module to address complex deformations in medical images effectively. Additionally, the elastic transformation from data augmentation techniques is integrated into the model architecture to enhance its capability to handle multi-scale features and improve robustness to geometric variations in image registration. Experimental evaluations on two medical image registration datasets indicate the effectiveness of our approach, surpassing existing state-of-the-art methods in terms of registration accuracy and computational efficiency. These findings underscore the potential of RWKVMatch as a highly effective tool for medical image registration. © 2025 ieee.

关键词： deformable image registration elastic transformation multi-scale feature fusion Vison-RWKV

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MetaCon: Revitalizing internet Congestion Control with Meta-Reinforcement Learning

MetaCon: Revitalizing Internet Congestion Control with Meta-...

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2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Bai, He Li, Hui Que, Jianming Zhang, Minglong Chong, Peter Han Joo Sudheera, Kalupahana Liyanage Kushan Pei, Xinyuan School of Electronic and Computer Engineering Peking University Shenzhen China School of Computer Science Fuyao University of Science and Technology Fuzhou China Department of Electrical and Computer Engineering Mississippi State University Starkville United States School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Electrical and Information Engineering Faculty of Engineering University of Ruhuna Sri Lanka

ISBN: (纸本)9798350368741

Effective congestion control algorithms (CCAs) are crucial for the smooth operation of internet communication infrastructure. CCAs adjust transmission rates based on congestion signals, optimizing resource utilization and user experience. However, existing studies, both rule-based and learning-based CCAs, often struggle with generalization and underperform when deployed in real-world environments. When applied to unseen network conditions, hand-crafted schemes or pre-trained models may experience significant performance degradation. To address this challenge, we propose MetaCon, a novel adaptive internet congestion control approach based on meta-reinforcement learning. MetaCon leverages knowledge learned from prior scenarios to quickly adapt to new environments. Experimental results show that MetaCon outperforms existing algorithms by exhibiting superior generalization and achieving better transmission performance across a wide variety of network conditions. © 2025 ieee.

关键词： communication network congestion control meta-learning reinforcement learning transport protocol

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