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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4811 条记录，以下是11-20 订阅

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Unsupervised image-to-image Style Transfer via Dual-Condition Diffusion Models

Unsupervised Image-to-Image Style Transfer via Dual-Conditio...

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

作者： Fan, Anfei Yang, Jun Li, Wei Zhang, Chiyu College of Computer Science Sichuan Normal University Chengdu China

ISBN: (纸本)9798350368741

Style transfer is an artistic research topic within a series of generative tasks, and it is quite challenging to stably and reliably guide the generation of images with the expected content and style. Early methods followed a predefined style, defined by a reference image, and applied it to another image while preserving the latter's content structure, but the generated effects lacked high fidelity. As diffusion models have evolved, the content guidance for style transfer has transitioned from images to text, potentially leading to instance alterations. Generating with multiple conditions constrained in the latent space is challenging. Within the EDM framework, we have utilized cross-attention to design style and content embedding modules, overcoming instance alterations without the need for paired datasets and achieving high-resolution, high-fidelity results. Our experimental findings demonstrate that our architecture has advanced to the forefront of image-to-image style transfer capabilities. © 2025 ieee.

关键词： diffusion models image generation image-to-image style transfer

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image Integrity Checking Using Watermarking in Cloud Computing: A Review 3rd

Image Integrity Checking Using Watermarking in Cloud Computi...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Rani, Jyoti Nath, Rajender Department of Computer Science and Applications Kurukshetra University Kurukshetra Thanesar India

ISBN: (纸本)9789819726707

In today’s era of cloud computing, modification and tampering of digital images on cloud storage have turn out to be easier due to proliferation of digital image processing tools. Consequently, tamper detection and integrity checking of images on remote server emerged as huge concern. image watermarking techniques provide an efficient way to tackle such problems. This paper provides a systematic examination of existing watermarking methods currently in use in cloud computing environment. This paper also presents a comparative analysis of those techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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LLGS: Illuminating Gaussian Splatting via absorptance Modulation

LLGS: Illuminating Gaussian Splatting via absorptance Modula...

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

作者： Gan, Jianwen Li, Wenxin Zheng, Bo Wang, Chengliang Wu, Yingbo National Elite Institute of Engineering Chongqing University Chongqing China AI Lab Chongqing Changan Automobile Co. Ltd. Chongqing China College of Computer Science Chongqing University Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9798350368741

Low-light images are typically characterized by low pixel intensity and color distortion, presenting a significant challenge for accurate 3D reconstruction with 3D Gaussian Splatting (3DGS). Traditional 2D enhancement methods fail to maintain consistent illumination, affecting reconstruction quality. We propose Low-Light Gaussian (LLGS), which can directly leverage low-light images for 3D reconstruction and synthesizing normal-light novel views. LLGS incorporates absorptance to simulate light behavior in low-light conditions, assuming objects maintain normal illumination while reflected light intensity attenuates due to absorptance during rendering. This approach enables the capture of authentic color information in dimly lit scenes. LLGS outperforms current enhancement algorithms and Neural Radiance Fields (NeRF) in image quality and processing efficiency, making it highly effective for low-light 3D scene reconstruction and novel view synthesis. © 2025 ieee.

关键词： 3D gaussians Low-light image enhancement Novel view synthesis Radiance fields

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A Survey on Adversarial Text Attacks on Deep Learning Models in Natural Language processing 5th

A Survey on Adversarial Text Attacks on Deep Learning Models...

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5th international conference on Data Science, Machine Learning and applications

作者： Deepan, S. Torres-Cruz, Fred Placido-Lerma, Ruben L. Udhayakumar, R. Anuradha, S. Kapila, Dhiraj SR Univ Sch Comp Sci & Artificial Intelligence Warangal Telangana India Univ Nacl Altiplanode Puno Unit Stat & Comp Engn Puno Peru Univ Andina Nestor Caceres Velasquez Syst Engn Juliaca Peru VelTech Rangarajan Dr Sagunthala R&D Inst Sci & T Chennai Tamil Nadu India Sri Sairam Engn Coll Dept English Chennai Tamil Nadu India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Deep neural networks have recently seen a significant surge in adoption for different Artificial Intelligence technologies due to the development of powerful computer systems. However, because of the growing security concerns, they are susceptible to dangerous risks. Adversarial instances were initially discovered in the field of computer vision (CV), where systems were deceived by altering their initial inputs. In the field of natural language processing (NLP), additionally they occur. Several approaches are put up to address this gap and handle an extensive variety of NLP applications. We give an organized survey of these works in this *** text is distinct and meaningful in nature, in contrast to the image, which makes the creation of hostile assaults much more challenging. In this study, we present a thorough analysis of adversarial attacks and counterattacks in the textual domain. In order to make the essay self-contained, we examine related important works in computer vision and cover the fundamentals of NLP. We explore unresolved concerns to close the gap between current advancements and increasingly powerful adversarial assaults on NLP DNNs in our survey's conclusion.

关键词： NLP DNN adversarial text attacks adversarial defense textual data

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Performance Comparison of Different Digital image Filters Used for Biomedical Signals 8th

Performance Comparison of Different Digital Image Filters U...

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8th international conference on Internet of Things and Connected Technologies, ICIoTCT 2023

作者： Sudagani Jyothi Muthu Krishnammal, P. School of Electronics Engineering VIT—AP University Andhra Pradesh Amaravati India School of Electronics Engineering VIT—AP University Andhra Pradesh Amaravati India

ISBN: (纸本)9789819757855

Getting highly accurate output in biomedical data processing concerning biomedical signals and images is impossible because biomedical data are generated from various electronic and electrical resources that can deliver the data with noise. Filtering is widely used for signal and image processing applications in medical, multimedia, communications, biomedical electronics, and computer vision. The biggest problem in biomedical signal and image processing is developing a perfect filter for the system. Digital filters are more advanced in precision and stability than analog filters. Digital filters are getting more attention due to the increasing advancements in digital technologies. Hence, most medical image and signal processing techniques use digital filters for preprocessing tasks. This paper briefly explains various filters used in medical image and signal processing. MATLAB is a famous mathematical, analytical software with a platform and built-in tools to design filters and experiment with different inputs. Even though this paper implements filters like mean, median, weighted average, Gaussian, and bilateral in Python to verify their performance in terms of mean square error (MSE) and similarity index (SI), a suitable filter can be selected for biomedical applications by comparing their performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Median filters

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Enhanced Multimodal Conversational AI Using Speech and image Integration 5th

Enhanced Multimodal Conversational AI Using Speech and Image...

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5th international conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Dixit, Deepali Tyagi, Mihul Kandoi, Raunak Singh, Raghuraj School of Engineering and Technology Sharda University Greater Noida201310 India

ISBN: (纸本)9789819773701

The development of conversational artificial intelligence (AI) is examined in this research paper, with a focus on how speech and image recognition technologies can be combined to transform and interact with systems. The emphasis is on cutting-edge developments in multimodal conversational AI, particularly the smooth fusion of sophisticated image and speech processing methods to improve user experiences. The study addresses the applications, difficulties and potential applications of multimodal conversational AI by conducting a thorough review of the existing literature and case studies. By emphasizing the significance of enhancing voice recognition algorithms and utilizing advanced image processing techniques, the study promotes contextual understanding and customization. We have developed an innovative multimodal conversational AI system that integrates speech, text and image processing capabilities for seamless human–computer interactions. This study presents an improved multimodal conversational AI system that integrates several techniques, such as Google Text-to-Speech (gtts), speech recognition, sound playback and Convolutional Neural Networks (CNN). Our model architecture combines RNNs for sequential data analysis, CNNs for image processing and attention mechanisms for context extraction. Trained on diverse datasets, our model is optimized for accuracy and robustness. Evaluation against existing models demonstrates superior performance in terms of accuracy, speed and resource efficiency. Our study highlights how improved multimodal conversational AI can advance the field of human–computer interaction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A Convolutional Recurrent Mixer Network For Radar Meteorological image Super-Resolution

A Convolutional Recurrent Mixer Network For Radar Meteorolog...

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

作者： Pires, Rafael Gonçalves Santos, Daniel F.S. Calheiros, Roberto V. Papa, João Paulo Lee, Ik Hyun Bakshi, Sambit Muhammad, Khan Department of Computing São Paulo State University Bauru Brazil Meteorological Research Institute São Paulo State University Bauru Brazil Dept. of Mechatronics Engineering Tech University of Korea IKLAB Inc. Korea Republic of Dept. of CS&E National Institute of Technology Rourkela India School of Convergence Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9798350368741

image super-resolution (SR) focuses on reconstructing high-resolution images from their low-resolution counterparts, often affected by sensor limitations or environmental factors. Convolutional Neural Networks (CNNs) are state-of-the-art for SR tasks but computationally heavy. This paper introduces a novel CRMN (Convolutional Recurrent Mixer Network), a hybrid deep learning-based SR technique designed to address the complexity of CNNs, which is validated in the context of meteorological radar images. Experiments on public benchmark datasets (Berkley432 and T291) and our newly manually collected precipitation dataset from the Meteorological Research Institute (IPMET) show that our CRMN model provides competitive results compared to leading SR methods with significantly fewer parameters, making it a promising and practical solution for SR applications, particularly radar meteorology. © 2025 ieee.

关键词： Convolutional Neural Networks Deep Learning Meteorology Recurrent Mixer Network Super Resolution

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Towards Maximizing Semantic Coverage for image-Text Retrieval

Towards Maximizing Semantic Coverage for Image-Text Retrieva...

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

作者： Xu, Junhao Liu, Zheng Chen, Zhumin Shen, Fei School of Computer Science and Technology Shandong University China School of Computer Science and Technology Shandong University of Finance and Economics China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798350368741

To establish semantic associations between images and texts, existing image-Text Retrieval (ITR) methods primarily focus on fixed-scale fragments, which only identify explicit semantic categories. Consequently, semantic coverage is constrained, leading to the omission of certain semantic associations. To enlarge the semantic coverage, we propose the Semantic Coverage-Aware Network (SCA-Net). First, explicit semantic categories are identified by SCA-Net through analyzing the semantic membership of visual and textual fragments, thereby establishing more precise explicit semantic associations. Second, implicit semantic categories are identified by SCA-Net via adaptively aggregating visual and textual fragments across various scales using a co-occurrence-aware router, thereby significantly expanding the semantic coverage and establishing complete semantic associations. Third, image-text similarity is calculated using the attention mechanism over a broader range of semantic coverage. Extensive experiments demonstrate that SCA-Net significantly enhances ITR performance compared to state-of-the-art methods by maximizing semantic coverage. © 2025 ieee.

关键词： Co-occurrence-aware router image-text retrieval Semantic association Semantic category Semantic coverage

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High-Fidelity Stereoscopic image Rain Removal with Texture Integrity and Disparity Consistency

High-Fidelity Stereoscopic Image Rain Removal with Texture I...

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

作者： Wei, Yanyan Zhang, Zhao Zhao, Zhong-Qiu Zhao, Yang Hong, Richang Yang, Yi Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei University of Technology Hefei China The College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350368741

This paper tackles the challenge of stereoscopic image rain removal by focusing on enhancing texture integrity and disparity consistency. Existing stereoscopic rain removal techniques often fall short due to 1) disruptions in texture coherence caused by complex rain streaks, and 2) inaccuracies in disparity estimation from inadequate feature fusion. To overcome these limitations, we introduce the StereoIRR method, which incorporates: 1) a Long-range and Cross-view Interaction (LCI) framework that preserves texture integrity by mitigating rain's adverse effects on stereoscopic features, and 2) a Dual-view Mutual Attention mechanism that ensures disparity consistency by generating precise mutual attention maps for cross-view feature fusion. Our approach not only maintains the integrity of stereoscopic textures but also significantly reduces errors in disparity estimation. Extensive experiments demonstrate that StereoIRR consistently outperforms state-of-the-art monocular and stereoscopic methods on multiple benchmark datasets. © 2025 ieee.

关键词： Dual-view mutual attention image deraining StereoIRR Stereoscopic and multiview processing

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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

作者： Zhang, Pei Wang, Dong Wu, Chanyue Yang, Jing Kang, Lei Bai, Zongwen Li, Ying Shen, Qiang School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Yan'an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd. Qingdao China Department of Computer Science Aberystwyth University Aberystwyth United Kingdom

ISBN: (纸本)9798350368741

Hyperspectral image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques. © 2025 ieee.

关键词： Cross-scene classification Diffusion model Hyperspectral image Transformer

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