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检索条件"任意字段=2021 International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2021"

共 691 条记录，以下是171-180 订阅

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ENHANCED CONVERGENT PNP algorithms FOR image RESTORATION

ENHANCED CONVERGENT PNP ALGORITHMS FOR IMAGE RESTORATION

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IEEE international conference on image processing (ICIP)

作者： Terris, Matthieu Repetti, Audrey Pesquet, Jean-Christophe Wiaux, Yves Heriot Watt Univ Inst Sensors Signals & Syst Edinburgh EH14 4AS Midlothian Scotland Heriot Watt Univ Dept Actuarial Math & Stat Edinburgh EH14 4AS Midlothian Scotland Univ Paris Saclay INRIA Cent Supelec Ctr Visual Comp F-91190 Gif Sur Yvette France

ISBN: (纸本)9781665441155

image restoration has long been one of the key research topics in image processing. Many mathematical approaches have been developed to solve this problem, e.g., variational methods, wavelet techniques, or Bayesian methods. With the widespread of neural network (NN) models in all the subdomains of data science, the performance limits of these methods are further pushed. One of the most successful strategies consists of plugging NNs in existing optimization algorithms. However, so doing raises several mathematical and practical challenges. One of the main issues is to secure the convergence of the resulting iterative scheme. Further questions concerning the characterization of the reached limit are also worth being addressed. In this paper, we show that the theory of maximally monotone operators allows us to bring insightful answers to these problems and to design firmly nonexpansive NNs;combining these with postprocessing NNs leads to excellent global restoration quality.

关键词： Neural networks forward-backward algorithm plug-and-play algorithm image restoration

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LMPDNET: TOF-PET LIST-MODE image RECONSTRUCTION USING MODEL-BASED DEEP LEARNING METHOD 30

LMPDNET: TOF-PET LIST-MODE IMAGE RECONSTRUCTION USING MODEL-...

引用

30th IEEE international conference on image processing (ICIP)

作者： Li, Chenxu Fang, Jingwan Hu, Rui Cui, Jianan Liu, Huafeng Zhejiang Univ State Key Lab Modern Opt Instrumentat Hangzhou 310027 Peoples R China

ISBN: (纸本)9781728198354

The integration of Time-of-Flight (TOF) information in the reconstruction process of Positron Emission Tomography (PET) improves image qualities. However, implementing the cutting-edge model-based deep learning methods for TOF-PET reconstruction is challenging due to the substantial memory requirements. In this study, we presented a novel model-based deep learning approach, LMPDNet, for TOF-PET reconstruction from list-mode data. We addressed the issue of real-time parallel computation of the projection matrix for list-mode data, and proposed an iterative model-based module that utilized a dedicated network model for list-mode data. Our experimental results indicated that the proposed LMPDNet outperformed traditional iteration-based TOF-PET list-mode reconstruction algorithms. Additionally, we compared the spatial and temporal consumption of list-mode data and sinogram data in model-based deep learning methods, demonstrating the superiority of list-mode data in model-based TOF-PET reconstruction.

关键词： TOF-PET reconstruction list-mode model-based deep learning

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Half-Split ResUNet Denoiser Based Deep Unrolling For Photon Limited image Deblurring 15

Half-Split ResUNet Denoiser Based Deep Unrolling For Photon ...

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15th international conference on signal processing and Communications (SPCOM)

作者： Kumar, Koyyada Dinesh Sahoo, Sujit Kumar Indian Inst Technol Goa Sch Elect Sci Ponda 403401 India

ISBN: (纸本)9798350350463;9798350350456

Photon-limited deblurring is a complex and demanding problem encountered in various applications where low-light conditions prevail. The scarcity of photons in such situations leads to the introduction of shot noise, resulting in a degradation of image quality. Solving this problem with Neural networks often involves constructing models empirically, making the behavior of the underlying architecture challenging to comprehend. A recent technique known as algorithm unrolling has enabled the connection of iterative algorithms with neural networks, where the Convolutional Neural Network (CNN) acts as a denoiser. This paper introduces a reduced parameter denoiser to enhance image quality and preserve finer details or avoid over-smoothing of the image during reconstruction. As a result, the unrolled model surpasses existing deblurring methods for improving image quality in low-light conditions. The proposed denoiser reduces the number of parameters by a factor of 3.84 and preserves the finer details while reconstructing. Our model improves computational efficiency and storage requirements compared to the state-of-the-art.

关键词： Photon limited Poisson deconvolution non-blind deblurring algorithm unfolding plug and play

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ICMSSP 2021 - 2021 6th international conference on Multimedia Systems and signal processing

ICMSSP 2021 - 2021 6th International Conference on Multimedi...

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6th international conference on Multimedia Systems and signal processing, ICMSSP 2021

ISBN: (纸本)9781450390378

The proceedings contain 11 papers. The topics discussed include: comparative study on image classification algorithm based on dimensionality reduction technologies and convolutional neural networks;MergeNet: single high dynamic range image reconstruction method;image aesthetic assessment based on emotion-assisted multi-task learning network;pyramid attention generative adversarial network for image compressed sensing;immersive teleconferencing with efficient implementation of fast frequency domain adaptive acoustical echo cancellation;a method based on LSTM network for detection of abnormal daily electricity;research on collaborative detection and location method of infrared and radar aircraft based on federated filter;and analysis of deep anomaly detection algorithms.

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PERCEPTUAL QUALITY ASSESSMENT FOR RECOGNIZING TRUE AND PSEUDO 4K CONTENT

PERCEPTUAL QUALITY ASSESSMENT FOR RECOGNIZING TRUE AND PSEUD...

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IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Zhu, Wenhan Zhai, Guangtao Min, Xiongkuo Yang, Xiaokang Zhang, Xiao-Ping Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Ryerson Univ Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9781728176055

To meet the imperative demand for monitoring the quality of Ultra High-Definition (UHD) content in multimedia industries, we propose an efficient no-reference (NR) image quality assessment (IQA) metric to distinguish original and pseudo 4K contents and measure the quality of their quality in this paper. First, we establish a database including more than 3000 4K images composed of natural 4K images together with upscaled versions interpolated from 1080p and 720p images by fourteen algorithms. To improve computing efficiency, our model segments the input image and selects three representative patches by local variances. Then, we extract the histogram features and cut-off frequency features in the frequency domain as well as the natural scenes statistic (NSS) based features from the representative patches. Finally, we employ support vector regressor (SVR) to aggregate these extracted features as an overall quality metric to predict the quality score of the target image. Extensive experimental comparisons using seven common evaluation indicators demonstrate that the proposed model outperforms the competitive NR IQA methods and has a great ability to distinguish true and pseudo 4K images.

关键词： image quality assessment (IQA) noreference (NR) Ultra High-Definition (UHD) frequency domain analysis natural scene statistics (NSS)

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Automatic Detection of Cowpea leaves Using image processing and Inception-V3 Model of Deep Learning 6

Automatic Detection of Cowpea leaves Using Image Processing ...

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6th IEEE international conference on signal processing, Computing and Control, ISPCC 2021

作者： Choudhary, Vijaya Guha, Paramita Tripathi, Kuldeep Mishra, Sunita Ghaziabad201002 India CSIR- Central Scientific Instruments Organization Delhi Centre India ICAR- National Bureau Of Plant Genetic Resources Delhi India

ISBN: (纸本)9781665425520

India is an agriculture-based country, and most of the population depends on agriculture for livelihoods. Cowpea plays a vital role to maintain healthy life because of its nutritious parameters. However, there is an urgent need to implement new technologies using Artificial Intelligence to enhance the productivity of crops and mitigate the cost, effort and loss of the agronomist. We have proposed a model based on deep learning algorithms to identify Cowpea leaves. For experiments, images were captured from the ICAR-NBPGR farm. We use TensorFlow and Keras platform under the umbrella of Deep Learning. Such methodology permits us 1) reorganization of the cowpea leaves based on image classification and 2) to estimate their weight. Inception model is type of application utilizes CNN based architecture to complete problems related to either binary or multiclass classification, that will again moderate by using transfer learning technique. We have initiate model for binary classification of Cowpea leaves with other leaves (Mango). © 2021 IEEE.

关键词： Convolutional neural networks

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Fast Low-light image Enhancement Algorithm Based on Fusion 6

Fast Low-light Image Enhancement Algorithm Based on Fusion

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6th international conference on signal and image processing, ICSIP 2021

作者： Guo, Junjie Li, Zhengping Xu, Chao Feng, Bo AnHui University AnHui Engineering Laboratory of Agro-Ecological Big Data School of Electronics and Information Engineering HeFei China

ISBN: (纸本)9780738133737

Low-light images generally exist in various situations, and the existing low-light image enhancement algorithms are poor in generality and have high computational complexity, and the comfort of the enhanced image is not satisfactory. This paper proposes a fast low-light image enhancement algorithm based on fusion. First, transform the passing space to HSV space to extract the V component. Then, according to the influence of Weber-Fechner's law on human visual senses, and effectively combining the characteristics of low-light images, a global adaptive brightness transformation function is constructed, and this function generates the first enhanced image. Then use CLAHE to increase the local contrast and generate a second enhanced image. Finally, an image fusion strategy is adopted to extract image details to fuse the image effectively. The proposed algorithm is fast and straightforward, can effectively improve the quality of low-light images, and better preserve its naturalness. Experimental results show that the proposed algorithm has achieved good results both subjectively and objectively. © 2021 IEEE.

关键词： image enhancement

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EXPOSING GAN-GENERATED FACES USING INCONSISTENT CORNEAL SPECULAR HIGHLIGHTS

EXPOSING GAN-GENERATED FACES USING INCONSISTENT CORNEAL SPEC...

引用

IEEE international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Hu, Shu Li, Yuezun Lyu, Siwei Univ Buffalo State Univ New York Buffalo NY 14260 USA Ocean Univ China Qingdao Peoples R China

ISBN: (纸本)9781728176055

Sophisticated generative adversary network (GAN) models are now able to synthesize highly realistic human faces that are difficult to discern from real ones visually. In this work, we show that GAN synthesized faces can be exposed with the inconsistent corneal specular highlights between two eyes. The inconsistency is caused by the lack of physical/physiological constraints in the GAN models. We show that such artifacts exist widely in high-quality GAN synthesized faces and further describe an automatic method to extract and compare corneal specular highlights from two eyes. Qualitative and quantitative evaluations of our method suggest its simplicity and effectiveness in distinguishing GAN synthesized faces.

关键词： Media Forensics GAN image Detection

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Research on Endoscopic image Enhancement Algorithm Based on Contrast Fusion 6

Research on Endoscopic Image Enhancement Algorithm Based on ...

引用

6th international conference on signal and image processing, ICSIP 2021

作者： Zhu, Yuzhe Xu, Chao Feng, Bo Fang, Shanshan AnHui University AnHui Engineering Laboratory of Agro-Ecological Big Data School of Electronics and Information Engineering HeFei China

ISBN: (纸本)9780738133737

image enhancement can improve doctors' perception of gastrointestinal tissues and blood vessels. This paper introduces an image enhancement method for medical endoscopy, which includes three stages: (1) improved contrast enhancement algorithm, using multiple iterative sharpening mask to enhance the edge contour of gastrointestinal tissues and blood vessels;(2) improved limiting threshold of limiting contrast adaptive histogram equalization (CLAHE);(3) improved contrast image fusion to preserve the good characteristics of improved contrast enhancement algorithm and improved CLAHE. The proposed method is applied to large data sets of low contrast white light endoscopes. The experimental results show that the endoscopic image enhancement algorithm based on contrast fusion is a feasible method to solve the defects between different image enhancement algorithms, because the image fusion results can effectively reduce the defects of contrast enhancement algorithm and CLAHE compared with the original image, such as detail loss, local contrast loss and pixel value loss. © 2021 IEEE.

关键词： image enhancement

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A Secure Wavelet based Watermarking Approach using Psycho-Visual Redundancy in image 7

A Secure Wavelet based Watermarking Approach using Psycho-Vi...

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7th international conference on advanced Computing and Communication Systems, ICACCS 2021

作者： Sinha, Ashish Panda, Jeebananda Delhi Technological University Technology Student Delhi India

ISBN: (纸本)9781665405201

The principal requirements of any digital watermarking algorithm are imperceptibility, robustness, security and capacity of the watermark [2], [9]. Imperceptibility and robustness are inherently conflicting in nature. Over numerous trials of digital watermarking of multimedia, it has been found that embedding a watermark with high strength also decreases its obscurity and makes the watermark more prone to detection. On the other hand, a watermark embedded with a low strength improves the imperceptibility but causes degradation of robustness against signal processing, geometric and feature distortive attacks. This paper proposes a secure approach for watermarking using the image Partitioning approach, which exploits the psycho-visual redundancy in the image to embed the watermark. Using the chaotic scrambling based Arnold Transform, watermark is first scrambled and then embedded in the sub-band of the image obtained using the Discrete Wavelet Transform and characterized by the lowest frequency. This improves the security as well as enhances the imperceptibility of the watermark, without significant deviation in visual appearance of the watermarked image from the cover image. Further, frequency domain embedding leads to better robustness and stability. The method is used to perform watermarking of grayscale images and performs well when subjected to common distortions, geometric, order-statistic filtering and signal processing attacks. This was determined by calculating the Peak signal to Noise Ratio of the watermarked cover image and the retrieved watermarks and the Normalized Cross-Correlation of the embedded and extracted watermarks. © 2021 IEEE.

关键词： Discrete wavelet transforms

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