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检索条件"任意字段=IEEE International Conference on Signal and Image Processing Applications"

共 79607 条记录，以下是41-50 订阅

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RAW Data: A Key Component for Effective Deepfake Detection

RAW Data: A Key Component for Effective Deepfake Detection

引用

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

作者： Husseinil, Sahar Dugelay, Jean-Luc Department of Digital Security EURECOM Biot France Docaposte Biometrics Lab Biot France

ISBN: (纸本)9798350368741

Current deepfake detection methods are prone to overfitting to specific deepfake artifacts and often struggle with genuine images that have undergone compression and other image processing operations. These processes can obscure indicators of forgery, leading to inaccurate decisions. This paper aims to redefine the boundary between real and fake images by narrowing the definition of authentic samples to a stage closer to the radiance of the scene as captured by the sensor, prior to any transformations by an image signal Processor (ISP). Our proposed method bypasses ISP processing steps, such as denoising, white balance, and de-mosaicing, which are embedded in camera hardware. This unaltered preservation makes raw data an ideal starting point for deepfake detection. Given the scarcity of large-scale datasets designed for training on raw images, we propose a methodological approach to train our model on raw image data. Our method demonstrated state-of-the-art performance on the CDF dataset and showed competitive results across other RGB domain deepfake detection datasets. The model developed in this study is available at https://***/DeepFaux/Deepfake-Detection-with-RAW-Data. © 2025 ieee.

关键词： deepfake detection image manipulation ISP

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Advanced Deep Learning Strategies for Breast Cancer image Analysis 6

Advanced Deep Learning Strategies for Breast Cancer Image An...

引用

6th ieee international conference on image processing, applications and Systems, IPAS 2025

作者： Slimi, Houmem Cherif, Imen Abid, Sabeur Sayadi, Mounir University Of Tunis Dept. Electrical Engineering Tunis Tunisia University Of Tunis Dept. Electronics Tunis Tunisia

ISBN: (纸本)9798331506520

Breast cancer (BC) is a leading cause of cancer-related deaths in women, but early detection significantly improves survival rates. Recently, deep learning neural networks have shown potential for enhancing BC screening, identification, and classification. In this research, we propose a modified deep learning model incorporating customized architecture adjustments and advanced data augmentation techniques for early BC detection and classification from mammography images. Our model achieves a 40% increase in accuracy compared to state-of-the-art transfer learning methods on the MIAS and SA datasets, demonstrating improved robustness and precision. However, while our results are promising, the limited size and diversity of the datasets suggest that further studies are needed to validate the generalizability of the model across broader, more varied data sources. © 2025 ieee.

关键词： Lung cancer

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OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation

OCTAMamba: A State-Space Model Approach for Precision OCTA V...

引用

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

作者： Zou, Shun Zhang, Zhuo Gao, Guangwei College of Artificial Intelligence Nanjing Agricultural University Nanjing China College of Computer Science and Technology National University of Defense Technology Changsha China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9798350368741

Optical Coherence Tomography Angiography (OCTA) is a crucial imaging technique for visualizing retinal vasculature and diagnosing eye diseases such as diabetic retinopathy and glaucoma. However, precise segmentation of OCTA vasculature remains challenging due to the multi-scale vessel structures and noise from poor image quality and eye lesions. In this study, we proposed OCTAMamba, a novel U-shaped network based on the Mamba architecture, designed to segment vasculature in OCTA accurately. OCTAMamba integrates a Quad Stream Efficient Mining Embedding Module for local feature extraction, a Multi-Scale Dilated Asymmetric Convolution Module to capture multi-scale vasculature, and a Focused Feature Recalibration Module to filter noise and highlight target areas. Our method achieves efficient global modeling and local feature extraction while maintaining linear complexity, making it suitable for low-computation medical applications. Extensive experiments on the OCTA 3M, OCTA 6M, and ROSSA datasets demonstrated that OCTAMamba outperforms state-of-the-art methods, providing a new reference for efficient OCTA segmentation. Code is available at https://***/zs1314/OCTAMamba. © 2025 ieee.

关键词： Mamba OCTA image State Space Model

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TextureDiffusion: Target Prompt Disentangled Editing for Various Texture Transfer

TextureDiffusion: Target Prompt Disentangled Editing for Var...

引用

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

作者： Su, Zihan Zhuang, Junhao Yuan, Chun Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9798350368741

Recently, text-guided image editing has achieved significant success. However, existing methods can only apply simple textures like wood or gold when changing the texture of an object. Complex textures such as cloud or fire pose a challenge. This limitation stems from that the target prompt needs to contain both the input image content and , restricting the texture representation. In this paper, we propose TextureDiffusion, a tuning-free image editing method applied to various texture transfer. Initially, the target prompt is directly set to "", making the texture disentangled from the input image content to enhance texture representation. Subsequently, query features in self-attention and features in residual blocks are utilized to preserve the structure of the input image. Finally, to maintain the background, we introduce an edit localization technique which blends the self-attention results and the intermediate latents. Comprehensive experiments demonstrate that TextureDiffusion can harmoniously transfer various textures with excellent structure and background preservation. Code is publicly available at https://***/THU-CVML/TextureDiffusion. © 2025 ieee.

关键词： AIGC Diffusion models image editing

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LLDB: Efficient Low-Light image Enhancement with Difffusion Bridge

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

引用

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

作者： Ma, Junlong Yue, Conghan Peng, Zhengwei Zhang, Dongyu Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

ISBN: (纸本)9798350368741

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. © 2025 ieee.

关键词： Diffusion Bridge Diffusion Model Low-Light image Enhancement

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Advancing SAR image Robustness: Integrating Diffusion Models for Adversarial Purification and Speckle Noise Suppression

Advancing SAR Image Robustness: Integrating Diffusion Models...

引用

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

作者： Xie, Ganglin Lu, Haobo Zhang, Xinze He, Kun School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350368741

Synthetic Aperture Radar (SAR) is a powerful tool for ground target detection, but SAR images often suffer from coherent speckle noise, which complicates automatic target recognition (ATR). Recent advances in deep learning have shown promise in SAR-ATR, yet there is a notable gap in addressing adversarial attacks and despeckling challenges. This study presents SAR Adversarial Diffusion Purification (SAR-ADP), a novel model designed to enhance SAR image analysis by tackling both adversarial attacks and coherent speckle noise. Our main contributions include pioneering methods for SAR image adversarial defense, simultaneous adversarial noise removal and despeckling, guidance mechanisms within the diffusion model, and a streamlined architecture for SAR image purification. SAR-ADP integrates adversarial defense strategies, diffusion modeling, and despeckling techniques, resulting in significant improvements in image recognition accuracy and post-processing. Empirical results validate its effectiveness, achieving a robustness accuracy of approximately 97% and a standard accuracy of around 99%. © 2025 ieee.

关键词： adversarial defense despeckling Diffusion model Synthetic Aperture Radar image target recognition

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On-the-fly image Compression Using Overfitted Shallow CNN Autoencoders 6

On-the-fly Image Compression Using Overfitted Shallow CNN Au...

引用

6th ieee international conference on image processing, applications and Systems, IPAS 2025

作者： El Gallab, John Ganna, Abdelwahab Kassem, Abdallah Makram, Hend Muawad, Martina Mikhael, Mina Elbatt, Tamer The American University in Cairo Computer Science and Engineering Cairo Egypt

ISBN: (纸本)9798331506520

In the era of pervasive Internet use, managing large volumes of image data becomes crucial. To mitigate storage and bandwidth costs, image compression plays a pivotal role. Traditional image compression techniques like JPEG and PNG, while widely used, may suffer from limited compression rates. In this work, we propose a novel approach using shallow Convolutional Neural Network (CNN) autoencoders for on-the-fly image compression. Our model aims to achieve high compression rates with improved image quality compared to classical methods. Additionally, it supports decompression on the CPU in realtime, making no assumptions about client-side computational resources. We present a comprehensive methodology, including architecture design, experiments, and performance metrics. Our results demonstrate the effectiveness of the proposed approach, providing a competitive alternative for online content compression and decompression that outperforms JPEG in image quality assessment metrics at 33% compression rate. We also outperform other Learned image Compression (LIC) techniques in both the decompression time and number of trainable parameters with an improvement in the order of 10-times. © 2025 ieee.

关键词： Convolutional neural networks

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OSLO-IC: On-the-Sphere Learned Omnidirectional image Compression with Attention Modules and Spatial Context

OSLO-IC: On-the-Sphere Learned Omnidirectional Image Compres...

引用

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

作者： Wawerek-López, Paul Bidgoli, Navid Mahmoudian Frossard, Pascal Kaup, André Maugey, Thomas Multimedia Communications and Signal Processing Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Lausanne Switzerland Rennes France

ISBN: (纸本)9798350368741

Developing effective 360-degree (spherical) image compression techniques is crucial for technologies like virtual reality and automated driving. This paper advances the state-of-the-art in on-the-sphere learning (OSLO) for omnidirectional image compression framework by proposing spherical attention modules, residual blocks, and a spatial autoregressive context model. These improvements achieve a 23.1% bit rate reduction in terms of WS-PSNR BD rate. Additionally, we introduce a spherical transposed convolution operator for upsampling, which reduces trainable parameters by a factor of four compared to the pixel shuffling used in the OSLO framework, while maintaining similar compression performance. Therefore, in total, our proposed method offers significant rate savings with a smaller architecture and can be applied to any spherical convolutional application. © 2025 ieee.

关键词： 360-degree end-to-end compression image compression spherical transposed convolution

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Learning Two-factor Representation for Magnetic Resonance image Super-resolution

Learning Two-factor Representation for Magnetic Resonance Im...

引用

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

作者： Wei, Weifeng Chen, Heng Su, Pengxiang School of Information Engineering Nanchang University Nanchang China School of Mathematics and Computer Science Nanchang University Nanchang China School of Software Nanchang University Nanchang China

ISBN: (纸本)9798350368741

Magnetic Resonance Imaging (MRI) requires a trade-off between resolution, signal-to-noise ratio, and scan time, making high-resolution (HR) acquisition challenging. Therefore, super-resolution for MR image is a feasible solution. However, most existing methods face challenges in accurately learning a continuous volumetric representation from low-resolution image or require HR image for supervision. To solve these challenges, we propose a novel method for MR image super-resolution based on two-factor representation. Specifically, we factorize intensity signals into a linear combination of learnable basis and coefficient factors, enabling efficient continuous volumetric representation from low-resolution MR image. Besides, we introduce a coordinate-based encoding to capture structural relationships between sparse voxels, facilitating smooth completion in unobserved regions. Experiments on BraTS 2019 and MSSEG 2016 datasets demonstrate that our method achieves state-of-the-art performance, providing superior visual fidelity and robustness, particularly in large up-sampling scale MR image super-resolution. © 2025 ieee.

关键词： Coordinate-based encoding MR image super-resolution Two-factor representation

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Simulation Verification Method for image Sensor Interface processing Unit Based on UVM 5

Simulation Verification Method for Image Sensor Interface Pr...

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5th ieee international conference on Power, Electronics and Computer applications, ICPECA 2025

作者： Ma, Wenji Beijing Sunwise Information Technology Ltd. Department of FPGA Verification Organization Beijing China

ISBN: (纸本)9798331533694

image sensors are widely employed across various industries, including smart mobile devices, autonomous vehicles, and surveillance systems. The image Sensor Interface processing Unit is typically characterized by a high degree of complexity to support diverse image sensor interfaces. Consequently, the effective and efficient verification of this unit is essential for upholding project quality and adhering to timelines. This article presents a simulation verification methodology for the image Sensor Interface processing Unit utilizing the Universal Verification Methodology (UVM). It explores the features of different sensor interfaces, the architectural framework, core base classes, runtime mechanisms, and reusable components within the UVM verification platform. The study identifies functional verification points based on the specific characteristics and application contexts of the image Sensor Interface processing Unit and formulates testing incentives. By analyzing the simulation results, the approach ensures comprehensive coverage of all functional points, achieving a 100% functional coverage rate. The testing platform is systematically designed to provide robust portability while meeting fundamental functional verification requirements, thereby serving as a reference for the development of future module verification environments. © 2025 ieee.

关键词： Reusability

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