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共 25554 条记录，以下是381-390 订阅

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LIGHTING image/VIDEO STYLE TRANSFER METHODS BY ITERATIVE CHANNEL PRUNING 49

LIGHTING IMAGE/VIDEO STYLE TRANSFER METHODS BY ITERATIVE CHA...

引用

49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Wu, Kexin Tang, Fan Liu, Ning Deussen, Oliver Le, Thi-Ngoc-Hanh Dong, Weiming Lee, Tong-Yee Jilin Univ Jilin Jilin Peoples R China ICT CAS Beijing Peoples R China Midea Grp Beijiaozhen Peoples R China Univ Konstanz Constance Germany Natl Cheng Kung Univ Tainan Taiwan CASIA Beijing Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Deploying style transfer methods on resource-constrained devices is challenging, which limits their real-world applicability. To tackle this issue, we propose using pruning techniques to accelerate various visual style transfer methods. We argue that typical pruning methods may not be well-suited for style transfer methods and present an iterative correlation-based channel pruning (ICCP) strategy for encoder-transform-decoder-based image/video style transfer models. The correlation-based channel regularization preserves the feature distributions for content and style references, and the iterative pruning strategy prevents layer collapse when pruning on the encoder-decoder structure. Experiments demonstrate that the proposed ICCP can generate visual competitive results compared to SOTA style transfer methods and significantly reduces the number of parameters (at least 70K) and inference time. Model is available at https://***/wukx-wukx/ICCP.

关键词： visual style transfer model pruning

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Cheque Information processing and Validation using Deep Learning 2

Cheque Information Processing and Validation using Deep Lear...

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2nd ieee Global conference on communications and Information Technologies, GCCIT 2024

作者： Suganthi, K. Shenoy, Darshan N. Vellore Institute of Technology School of Electronics Engineering Chennai600127 India

ISBN: (纸本)9798350388916

Understanding document images, such as invoices, is a difficult task. It necessitates reading the text as well as comprehending the document's general structure. Conventional techniques first extract text using Optical Character Recognition (OCR), yet this method has drawbacks. In addition to being computationally costly, OCR can have trouble converting between various languages and document formats and introduce errors that can compound throughout the analysis *** is an OCR-free VDU (visual Document Understanding) model that challenges this paradigm. It does not even bother with the text extraction stage;it analyzes the document image straight. This creative strategy produces notable advancements. Donut performs at the cutting edge, which means it is quick and precise. This is a significant development in the realm of visual document understanding since it enables Donut to handle a greater variety of languages and document types. The model has been tested using BLEU scores and ROUGE scores over 3 parameters namely - payee name, amount in words and amount in figure. The model has also been tested over variety of sample sizes ranging from 50 to 1000. © 2024 ieee.

关键词： Optical character recognition

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Enhancing image Quality through Fractional Order Unsharp Masking with Particle Swarm Optimization 2

Enhancing Image Quality through Fractional Order Unsharp Mas...

引用

2nd ieee International conference on Networking and communications, ICNWC 2024

作者： Gamini, Sridevi Kamisetti, Kavya Sri Nallamilli, Priyanka Darisi, Sai Pavan Venkatapathi, Venkatesh Aditya Engineering College Dept. of ECE Surampalem India

ISBN: (纸本)9798350365269

This paper presents a novel approach to image enhancement using Fractional-Order Unsharp Masking (FOUM) combined with Particle Swarm Optimization (PSO). The proposed method aims to improve the quality of digital images by enhancing their contrast and details. The PSO is employed to optimize the parameters involved in the FOUM process which includes the fractional order, and the weights for combining Laplacian and fractional differential filter. In this approach, Grunwald-Letnikov fractional differential filter is employed. Experimental results demonstrate that the proposed method outperforms traditional image enhancement techniques in terms of entropy and average values, yielding images with enhanced visual quality and improved feature preservation. © 2024 ieee.

关键词： image enhancement

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An Approach for Enhancement of Low Contrast Gray Scale image Using Fuzzy Logic and Sigmoid Function 2

An Approach for Enhancement of Low Contrast Gray Scale Image...

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2nd ieee International conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Kumar, Abhishek Kumar, Sanjeev Sarala Birla University Department of Electronics & Communication Ranchi India

ISBN: (纸本)9798350372847

Contrast enhancement plays a pivotal role in image processing, particularly for improving the visual quality of images in various applications. This paper presents an approach for enhancing such images by employing a point processing with the combination of Gaussian membership function for fuzzification and sigmoid function for modification. First, the image is fuzzified using Gaussian membership functions to transform pixel intensities into fuzzy sets, allowing for a more robust representation of the image's underlying characteristics. This fuzzification process effectively captures the uncertainty associated with pixel values in low contrast regions. Subsequently, a sigmoid function is applied to the fuzzified image to modify the pixel intensities. Experimental results demonstrate the efficacy of the proposed method in enhancing low contrast images, as evidenced by significant improvements in visual quality and quantitative metrics such as peak signal-to-noise ratio (PSNR) and entropy. The proposed approach offers a promising solution for enhancing low contrast images across various applications, contributing to advancements in image processing and analysis. © 2024 ieee.

关键词： Fuzzy logic

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Motor Focus: Fast Ego-Motion Prediction for Assistive visual Navigation 20

Motor Focus: Fast Ego-Motion Prediction for Assistive Visual...

引用

20th International conference on Body Sensor Networks

作者： Wang, Hao Qin, Jiayou Chen, Xiwen Bastola, Ashish Suchanek, John Gong, Zihao Razi, Abolfazl Clemson Univ Sch Comp Clemson SC 29634 USA Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ USA Tokai Univ Sch Cultural & Social Studies Tokai Ibaraki Japan

ISBN: (纸本)9798331530143

Assistive visual navigation systems for visually impaired individuals have become increasingly popular thanks to the rise of mobile computing. Most of these devices work by translating visual information into voice commands. In complex scenarios where multiple objects are present, it is imperative to prioritize object detection and provide immediate notifications for key entities in specific directions. This brings the need for identifying the observer's motion direction (ego-motion) by merely processing visual information, which is the key contribution of this paper. Specifically, we introduce Motor Focus, a lightweight image-based framework that predicts the ego-motion -the humans' (and humanoid machines') movement intentions based on their visual feeds, while filtering out camera motion without any camera calibration. To this end, we implement an optical flow-based pixel-wise temporal analysis method to compensate for the camera motion with a Gaussian aggregation to smooth out the movement prediction area. Subsequently, to evaluate the performance, we collect a dataset including 50 clips of pedestrian scenes in 5 different scenarios. We tested this framework with classical feature detectors such as SIFT and ORB to show the comparison. Our framework demonstrates its superiority in speed (> 40FPS), accuracy (MAE = 60pixels), and robustness ( SNR = 23dB), confirming its potential to enhance the usability of vision-based assistive navigation tools in complex environments. The code is publicly available at https://***/***/project/VisionGPT.

关键词： Assistive visual Navigation Motion Analysis Vision Enhancement image processing

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IMPROVING GENERALIZATION IN FACIAL MANIPULATION DETECTION USING image NOISE RESIDUALS AND TEMPORAL FEATURES 30

IMPROVING GENERALIZATION IN FACIAL MANIPULATION DETECTION US...

引用

30th ieee International conference on image processing (ICIP)

作者： Atamna, Mehdi Tkachenko, Iuliia Miguet, Serge Univ Lyon 2 Univ Lyon UCBL Centrale LyonCNRSINSA LyonLIRISUMR5205 F-69676 Bron France

ISBN: (纸本)9781728198354

The high visual quality of modern deepfakes raises significant concerns about the trustworthiness of digital media and makes facial tampering detection more challenging. Although current deep learning-based deepfake detectors achieve excellent results when tested on deepfake images or image sequences generated using known methods, generalization-where a trained model is tasked with detecting deepfakes created with previously unseen manipulation techniques-is still a major challenge. In this paper, we investigate the impact of training spatial and spatio-temporal deep learning network architectures in the image noise residual domain using spatial rich model (SRM) filters on generalization performance. To this end, we conduct a series of tests on the manipulation methods of the FaceForensics++, DeeperForensics-1.0 and Celeb-DF datasets, demonstrating the value of image noise residuals and temporal feature exploitation in tackling the generalization task.

关键词： Deepfake detection video manipulation detection image forensics steganalysis features

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EFFECTIVE image TAMPERING LOCALIZATION VIA ENHANCED TRANSFORMER AND CO-ATTENTION FUSION 49

EFFECTIVE IMAGE TAMPERING LOCALIZATION VIA ENHANCED TRANSFOR...

引用

49th ieee International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Guo, Kun Zhu, Haochen Cao, Gang Commun Univ China Sch Comp & Cyber Sci Beijing 100024 Peoples R China Commun Univ China State Key Lab Media Convergence & Commun Beijing 100024 Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Powerful manipulation techniques have made digital image forgeries be easily created and widespread without leaving visual anomalies. The blind localization of tampered regions becomes quite significant for image forensics. In this paper, we propose an effective image tampering localization network (EITLNet) based on a two-branch enhanced transformer encoder with attention-based feature fusion. Specifically, a feature enhancement module is deployed to enhance the feature representation ability of the transformer encoder. The features extracted from RGB and noise streams are fused effectively by the coordinate attention-based fusion module at multiple scales. Extensive experimental results verify that the proposed scheme achieves the state-of-the-art generalization ability and robustness in various benchmark datasets. Code is public at https://***/multimediaFor/EITLNet.

关键词： image forensics Tampering localization Transformer Feature enhancement Coordinate attention

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EXPLOITING SELF-SUPERVISED CONSTRAINTS IN image SUPER-RESOLUTION

EXPLOITING SELF-SUPERVISED CONSTRAINTS IN IMAGE SUPER-RESOLU...

引用

ieee International conference on Multimedia and Expo (ICME)

作者： Wu, Gang Jiang, Junjun Jiang, Kui Liu, Xianming Harbin Inst Technol Fac Comp Harbin 150001 Peoples R China

ISBN: (纸本)9798350390155;9798350390162

Recent advances in self-supervised learning, predominantly studied in high-level visual tasks, have been explored in low-level image processing. This paper introduces a novel self-supervised constraint for single image super-resolution, termed SSC-SR. SSC-SR uniquely addresses the divergence in image complexity by employing a dual asymmetric paradigm and a target model updated via exponential moving average to enhance stability. The proposed SSC-SR framework works as a plug-and-play paradigm and can be easily applied to existing SR models. Empirical evaluations reveal that our SSC-SR framework delivers substantial enhancements on a variety of benchmark datasets, achieving an average increase of 0.1 dB over EDSR and 0.06 dB over SwinIR. In addition, extensive ablation studies corroborate the effectiveness of each component in our SSC-SR framework. Codes are available at https://***/Aitical/SSCSR.

关键词： image Super-Resolution Contrastive Learning Self-Supervised Learning

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Optimizing Multimodal image Fusion: A Novel Approach with Nystrom Attention Mechanisms in Transformer Models

Optimizing Multimodal Image Fusion: A Novel Approach with Ny...

引用

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

作者： Zeng, Yuqin Wen, Ze Fan, Shuqian Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Chongqing School University of Chinese Academy of Sciences Chongqing China Chongqing China

ISBN: (纸本)9798350368741

This work proposed a new model based on transformers for multimodal image fusion, with explicit attention paid to fusing infrared and visible images toward enhanced detail and information content. This method, which incorporates Nystrom attention and skip connections, optimizes the fusion process so that important features of both modalities are preserved. Traditional models find it hard to keep details in the content and introduce artifacts. On the contrary, this model can efficiently cope with these challenges, as it makes use of an attention mechanism that is computationally highly effective. This will efficiently fuse large-scale image data and may be useful in those fields where very detailed and comprehensive visual information is demanded. The detailed evaluations prove that our model has superior performance over the current state-of-the-art methods in texture detail preservation, thermal differentiation, and overall image quality, which possesses great potential for advanced imaging applications. © 2025 ieee.

关键词： attention mechanism image fusion multimodal imaging transformer

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LLIEFORMER: A LOW-LIGHT image ENHANCEMENT TRANSFORMER NETWORK WITH A DEGRADED RESTORATION MODEL 30

LLIEFORMER: A LOW-LIGHT IMAGE ENHANCEMENT TRANSFORMER NETWOR...

引用

30th ieee International conference on image processing (ICIP)

作者： Yi, Xunpeng Wang, Yuxuan Zhao, Yizhen Yan, Jia Zhang, Weixia Wuhan Univ Elect Informat Sch Wuhan Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai Peoples R China

ISBN: (纸本)9781728198354

Low-light image enhancement aims at improving human perception or the effectiveness of computer vision tasks of images taken in dark. The low-light images are usually seriously lack in visual information. To tackle this problem, we propose a general Low-light image Enhancement Transformer Network (LLIEFormer) with a degraded restoration model in this paper. The network of LLIEFormer synthesizes the advantages of Transformer to extract global information and convolutional neural networks to capture local details. We conduct extensive experiments on various low-illumination enhanced datasets including PairL1.6K and FiveK to demonstrate the effectiveness of our method. The results show that our LLIEFormer has better performance and wider applicability than other advanced methods. Our code will be available at https://***/xunpengyi/LLIEFormer.

关键词： Low-light image enhancement image restoration Transformer network

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