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

共 20489 条记录，以下是841-850 订阅

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Adaptive Steganography Approach in RGB images based on Color Channels to Enhance Stego image Quality 16

Adaptive Steganography Approach in RGB Images Based on Color...

引用

16th ieee international conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Bunyamin, Rihan Farih D'Layla, Adifa Widyadhani Chanda De La Croix, Ntivuguruzwa Jean Ahmad, Tohari Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia Institut Teknologi Sepuluh Nopember Surabaya Indonesia University of Rwanda Department of Informatics Kigali Rwanda

ISBN: (纸本)9798331505264

The need to secure sensitive information has rapidly advanced based on the increased vulnerability of the data transmitted over the public network. Aligning with securing the data transmission, numerous steganographic techniques have been proposed, often using digital images, with the primary goal of finding an optimal balance between sensitive information size and the quality of the carrier image. Nonetheless, many existing methods present a drawback of perceptible secret data when concealed in the cover image's pixels, making them less practical for modern data-intensive applications. This article provides a new adaptive method to improve the quality of the data carrier image to seek the integrity of the sensitive data transmitted. The proposed approach utilises a modulus function to the Red, Green, and Blue (RGB) image channels to selectively embed secret bits from the sensitive data. The Blue channel hosts the most data due to its least sensitivity to changes. Experimental results indicate promising results with an average Peak signal-to-Noise Ratio (PSNR) of 57.146 (dB), reflecting a carrier image close to the original cover image. Compared to the existing works, the proposed method outperforms with a superior stego image quality. © 2024 ieee.

关键词： RGB color model

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Robust Fault Diagnostics of Industrial Motors: The signal-to-image Approaches for Sensor Data Using Multi-Task Transformers with Diverse Attention Mechanism 3

Robust Fault Diagnostics of Industrial Motors: The Signal-to...

引用

3rd ieee international conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Mukhopadhyay, Sudipta Kar, Indrajit Ralte, Zonunfeli Zensar Technologies Bangalore India

ISBN: (纸本)9798350313703

Vibration analysis serves as a valuable method to identify defects within mechanical systems. This process involves monitoring the auditory signals generated by the system and discerning patterns that signify potential issues. Nevertheless, due to the intricate nature of motors, relying solely on one modality (such as acoustic signals) might prove inadequate for comprehensively detecting all faults. Our research introduces a novel approach to enhance fault detection in mechanical systems, specifically focusing on motors. A more comprehensive system health assessment is achieved by utilizing multi-modal vibration analysis, combining acoustic signals and heat signatures. Effective information fusion is facilitated by integrating attention-based transformers, capable of capturing cross-modal correlations. The proposed neural network employs classification and regression loss functions for fault identification and severity assessment respectively. Evaluation on a motor fault dataset demonstrates significant classification accuracy improvements compared to conventional deep and machine learning methods. Incorporating a signal-to-image transformation further aids in fault pattern recognition. This method offers a promising avenue for accurate and comprehensive fault diagnosis in mechanical systems, showcasing the potential of multi-modal analysis and attention mechanisms for industrial equipment maintenance. © 2024 ieee.

关键词： Fault detection

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Hiding Secret Data Using Reduced Differences within the Pixels of an image 2

Hiding Secret Data Using Reduced Differences within the Pixe...

引用

2nd ieee international conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Fath, Nadira Milha Nailul Aminy, Moh. Rosy Haqqy De La Croix, Ntivuguruzwa Jean Ahmad, Tohari Teknologi Sepuluh Nopember Department of Informatics surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia University of Rwanda Kigali Rwanda

ISBN: (纸本)9798350372892

In the modern era, where vast amounts of data traverse public networks, the risk of data breaches is significant. Steganography, a method of concealing data within digital objects like images, is employed to secure sensitive information during transmission. However, utilizing digital images for this purpose often involves a trade-off between image quality and data quantity. To address this challenge, this study introduces a novel steganographic approach based on adjacent pixel differentiation within digital cover images. By customizing pixel differences, the method identifies optimal embedding locations to minimize distortion in the resulting stego image. Experimental results demonstrate an average peak signal-tonoise ratio (PSNR) of 65.97 decibels (dB), indicating high stego image quality. This metric serves as a precise measure of the proposed method's effectiveness, with higher PSNR values denoting superior stego image quality. © 2024 ieee.

关键词： data protection digital images information security securing network infrastructure steganography

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ROI-based Deep image Compression with Swin Transformers 48

ROI-Based Deep Image Compression with Swin Transformers

引用

48th ieee international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Li, Binglin Liang, Jie Fu, Haisheng Han, Jingning Simon Fraser University School of Engineering Science Canada Xian Jiaotong University School of Microelectronics China Google Llc Mountain ViewCA United States

ISBN: (纸本)9781728163277

Encoding the Region Of Interest (ROI) with better quality than the background has many applications including video conferencing systems, video surveillance and object-oriented vision tasks. In this paper, we propose a ROI-based image compression framework with Swin transformers as main building blocks for the autoencoder network. The binary ROI mask is integrated into different layers of the network to provide spatial information guidance. based on the ROI mask, we can control the relative importance of the ROI and non-ROI by modifying the corresponding Lagrange multiplier λ for different regions. Experimental results show our model achieves higher ROI PSNR than other methods and modest average PSNR for human evaluation. When tested on models pre-trained with original images, it has superior object detection and instance segmentation performance on the COCO validation dataset. © 2023 ieee.

关键词： Learned image compression region-of-interest Swin transformers

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SFEMGN: image Denoising with Shallow Feature Enhancement Network and Multi-Scale ConvGRU 48

SFEMGN: Image Denoising with Shallow Feature Enhancement Net...

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48th ieee international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Wang, Qidong Guo, Lili Ding, Shifei Zhang, Jian Xu, Xiao China University of Mining and Technology School of Computer Science and Technology Xuzhou China Ministry of Education of the People's Republic of China Mine Digitization Engineering Research Center Xuzhou China

ISBN: (纸本)9781728163277

image denoising methods based on convolutional neural networks have been popular and achieved relatively excellent performance. However, most of the existing methods cannot fully obtain and use the shallow feature information when removing noise, and cannot better combine information between various network layers. In this paper, we propose an image denoising algorithm based on a feature enhancement network and multi-scale convGRU, named a shallow feature enhancement and multi-scale convGRU denoising network (SFEMGN), through an in-depth study of convolutional networks and GRU networks. We first propose a feature enhancement block to extract richer shallow features and enhance the protection of image details. Furthermore, the proposed SFEMGN integrates a multi-scale convolution GRU module, which can combine spatial features and temporal features at the same time. Comparative experiments and ablation studies demonstrate that our proposed model can achieve competitive performance in both gray and color image denoising tasks. © 2023 ieee.

关键词： convGRU feature enhancement network image denoising multi-scale

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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...

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

作者： Yin, Xiangchen Yu, Zhenda Gao, Xin Sun, Xiao Institute of Artificial Intelligence Hefei Comprehensive National Science Center University of Science and Technology of China Hefei230601 China Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230601 China School of Vehicle and Mobility Tsinghua University Beijing100084 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China

ISBN: (纸本)9798350368741

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. © 2025 ieee.

关键词： Discrete Cosine Transform Frequency Learning Low-light image Enhancement Transformer

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TASK-ADAPTIVE FEATURE MATCHING LOSS FOR image DEBLURRING 30

TASK-ADAPTIVE FEATURE MATCHING LOSS FOR IMAGE DEBLURRING

引用

30th ieee international conference on image Processing (ICIP)

作者： Chang, Chiao-Chang Yang, Bo-Cheng Liu, Yi-Ting Chen, Jun-Cheng Jhuo, I-Hong Lin, Yen-Yu Natl Yang Ming Chiao Tung Univ Hsinchu Taiwan Natl Tsing Hua Univ Hsinchu Taiwan Acad Sinica Taipei Taiwan Microsoft Redmond WA USA

ISBN: (纸本)9781728198354

image deblurring is a highly challenging and ill-posed image restoration problem. Contemporary deep learning-based approaches usually tackle this problem by exploiting the encoder-decoder-based models trained by the commonly used mean squared error loss with the feature matching loss as a regularization to obtain perceptual consistent restored results as the ground truths. We argue that since the general backbone models for computing feature matching loss are usually not trained on the image deblurring task, the loss lacks specific knowledge of blur and usually leads to suboptimal performance. To address this issue, we propose a task-adaptive feature matching loss for image deblurring where we synthesize blurred images in different blur extents and employ triplet loss to finetune the backbone model for learning specific blur priors. Then, we leverage the finetuned backbone to compute feature matching loss which can greatly enhance the existing image deblurring models for better perceptual results. With extensive experiments on the GoPro and RealBlur datasets, both qualitative and quantitative results show that the SOTA deblurring models trained with the proposed loss can effectively obtain better and sharper restored images in terms of various perceptual image quality metrics than the original models while maintaining comparable PSNR and SSIM performances.

关键词： CLIP feature matching loss image deblurring

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internet Model Construction of Football Sports Propagation based on Automatic Camera Action Collection and image Enhancement Algorithm 5

Internet Model Construction of Football Sports Propagation b...

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5th international conference on Inventive Computation Technologies (ICICT)

作者： Hu, Hongquan Liaocheng Univ Sch PE Liaoccheng Shandong Peoples R China

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

In this paper, a video signal acquisition system is designed based on the input capture function of the HCS12 single-chip microcomputer for automatic wonderful motion acquisition by the camera. In this system, the output signal of the CMOS camera is binarized, and a color restoration and image denoising network is designed to solve the problems of color distortion and noise amplification in the enhanced image. image quality and visual effects, extracting shape features of black guide lines. Analyze the wireless channel quality of football live broadcast in the network model from the overall, local and individual levels, in order to provide theoretical reference for players' passing performance evaluation and targeted preparation.

关键词： internet Model Construction Football Sports Propagation Automatic Camera Action image Enhancement Algorithm

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DreamVideo: High-Fidelity image-to-Video Generation with image Retention and Text Guidance

DreamVideo: High-Fidelity Image-to-Video Generation with Ima...

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

作者： Wang, Cong Gu, Jiaxi Hu, Panwen Guo, Yuanfan Dong, Xiao Xu, Hang Liang, Xiaodan School of Intelligent Engineering Sun Yat-sen University at Shenzhen Shenzhen China Noah'ark Lab Huawei Shanghai China School of Science and Engineering The Chinese University of Hong Kong at Shenzhen Shenzhen China School of Intelligent Engineering Sun Yat-sen University at Zhuhai Zhuhai China

ISBN: (纸本)9798350368741

image-to-video generation, which aims to generate a video starting from a given reference image, has drawn great attention. Existing methods frequently integrate semantic information from images or simply concatenate images, which often leads to low fidelity and flickering in the generated videos. To tackle these problems, we propose a high-fidelity image-to-video generation method by devising a frame retention branch based on a pre-trained video diffusion model, named DreamVideo. Our DreamVideo perceives the reference image via convolution layers and concatenates the features with the noisy latents as model input. By this means, the details of the reference image can be preserved to the greatest extent. In addition, by incorporating the designed double-condition classifier-free guidance, DreamVideo can generate high-quality videos of different actions by providing varying prompt texts. We conduct comprehensive experiments on the public datasets, and both quantitative and qualitative results indicate that our method outperforms the state-of-the-art method. © 2025 ieee.

关键词： computer vision controllable video generation deep learning diffusion model image-to-video generation

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Mosaic Method for Bistatic Acoustic images of Seafloor based on Pixel Weighting 6

Mosaic Method for Bistatic Acoustic Images of Seafloor Based...

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6th international conference on Information Communication and signal Processing, ICICSP 2023

作者： Su, Zhaohua Zhuo, Jie Sun, Chao School of Marine Science and Technology Northwestern Polytechnical University Shaanxi Key Laboratory of Underwater Information Technology Xi'an China

ISBN: (纸本)9798350339994

In a bistatic sonar imaging system for seafloor composed of a vertical transmitting array and a horizontally towed receiving array, the left-right ambiguity caused by the receiving array leads to false features in mosaicked image using a conventional method. In this paper, an improved image mosaic method based on pixel weighting is proposed. A sliding window is applied to filter the pixels in each frame that are above the mean value of the sub-window, and then the number of occurrences of each pixel is used as the weighted value for mosaicking images. Both numerical simulations and experimental results show that the proposed method can effectively suppress the ambiguities in the mosaicked image. © 2023 ieee.

关键词： bistatic acoustic imaging image mosaicking left-right ambiguity pixel weighting

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