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共 691 条记录，以下是151-160 订阅

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LIPSCHITZ-CONSTRAINED CONVOLUTIONAL LAYERS USING CONVEX PROJECTION 49

LIPSCHITZ-CONSTRAINED CONVOLUTIONAL LAYERS USING CONVEX PROJ...

引用

49th IEEE international conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Kumar, Bhartendu Chaudhury, Kunal N. Indian Inst Sci Dept Elect Engn Bengaluru 560012 India

ISBN: (纸本)9798350344868;9798350344851

The problem of training a convolutional neural network (CNN) with a stipulated Lipschitz bound comes up in applications such as adversarial robustness, stability of closed-loop controllers, and image reconstruction. The present work was motivated by Plug-and-Play (PnP) and Regularization-by-Denoising (RED) which use CNN denoisers for image reconstruction. It has been shown that the convergence of these iterative algorithms can be guaranteed by constraining the Lipschitz bound of the denoiser. We make the case that using a contractive CNN denoiser is a straightforward means to certify convergence. In particular, we show how a contractive CNN denoiser can be trained using convex projections within the paradigm of gradient-based learning and how the projection problem can be reduced to a tractable convex program. Apart from the theoretical guarantee, the regularization capacity of the trained denoiser is shown to be competitive with BM3D and DnCNN.

关键词： image reconstruction convolution Fourier transform Lipschitz bound convex projection

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REVERSIBLE DATA HIDING IN JPEG imageS FOR PRIVACY PROTECTION

REVERSIBLE DATA HIDING IN JPEG IMAGES FOR PRIVACY PROTECTION

引用

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

作者： Huang, Yuxuan Cao, Xin Wu, Hao-Tian Cheung, Yiu-ming South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Hong Kong Baptist Univ Dept Comp Sci Kowloon Tong Hong Kong Peoples R China

ISBN: (纸本)9781728176055

Recently, how to protect privacy in JPEG images has become an important issue in social networks. Besides encryption, reversible visual transformation has been studied for content and privacy protection. In this paper, an improved algorithm is proposed to conceal privacy information in JPEG images. By adopting face detection, the regions to be protected can be identified so that the DC and some AC coefficients in them are modified. The changes that have been made are reversibly saved in the other AC coefficients, and the block selection information is hidden into the whole image also by reversible data hiding. As a secret key is employed to control the process of transformation, the protected content can hardly be obtained without knowing the key. We conduct the proposed algorithm on a set of test images, and compare the performance with several existing algorithms in terms of information leakage, file size increment, and image quality. The experimental results show that improved image quality and less information leakage can be achieved with the proposed reversible transformation algorithm.

关键词： JPEG image face detection privacy protection shuffling reversible data hiding

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Depth Recovery from Single Monocular image in 3D Reconstruction and Visual Precise Positioning 4

Depth Recovery from Single Monocular Image in 3D Reconstruct...

引用

4th international conference on Information Communication and signal processing, ICICSP 2021

作者： Bao, Jianjun Wang, Haibo Fu, Xinzhu Luo, Ke Shen, Xiaolin Tiandi Changzhou Automation Co. Ltd CCTEG Changzhou Research Institute Changzhou China Jiangsu Urban and Rural Construction College Changzhou China

ISBN: (纸本)9781665407571

The depth information of the image is essential for accurate positioning. However, for the scale of the model, stricter standards are inevitable. This paper proposed Lower Layer Efficient Neural Network (LL-ENet) on the problems of requirements with light model, mostly using on the mobile devices. Firstly, The model reconstruct encoder by changing the convolution for less calculation, then classify the depth for efficient predictive ability. Finally, the result by experience shows that LL-EN et has a better effect on depth recovery with single monocular image in the outdoor scenes. Moreover the further experience shows that the proposed LL-ENet has better performance than other algorithms. © 2021 IEEE.

关键词： image reconstruction

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Multispectral images Deblurring via Interchannel Correlation Relationship 4

Multispectral Images Deblurring via Interchannel Correlation...

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4th international conference on Information Communication and signal processing, ICICSP 2021

作者： Shi, Yuetian Wang, Nan Yang, Fanchao Zhang, Geng Li, Siyuan Liu, Xuebin Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Key Laboratory of Spectral Imaging Technology CAS Xi'an China

ISBN: (纸本)9781665407571

image blur is very common in multispectral images, especially out-of-focus blur when the multispectral cameras are well focused on a specific channel. Therefore, it is important to recover the latent clear images from the blurred image. Many researchers have proposed a large number of methods for deblurring multispectral images. These algorithms assume that the relationship between channels is consistent. In fact, the relationship between different channels in multispectral images suffers from different blurring. Therefore it needs different processing for different channels. We propose a joint spatial and spectral filtering algorithm for multispectral images deblurring. We have introduced an adaptive Gaussian distribution as a constraint relationship between channels, which can effectively deblur the multispectral images. Extensive experiments show that our method achieves state-of-the-art results. © 2021 IEEE.

关键词： image enhancement

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LOW-RANK REGULARIZED JOINT SPARSITY FOR image DENOISING

LOW-RANK REGULARIZED JOINT SPARSITY FOR IMAGE DENOISING

引用

IEEE international conference on image processing (ICIP)

作者： Zha, Zhiyuan Wen, Bihan Yuan, Xin Zhou, Jiantao Zhu, Ce Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Nokia Bell Labs Murray Hill NJ 07974 USA Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9781665441155

Nonlocal sparse representation models such as group sparse representation (GSR), low-rankness and joint sparsity (JS) have shown great potentials in image denoising studies, by effectively exploiting image nonlocal self-similarity (NSS) property. Popular dictionary-based JS algorithms apply convex JS penalties in their objective functions, which avoid NP-hard sparse coding step, but lead to only approximately sparse representation. Such approximated JS models fail to impose low-rankness of the underlying image data, resulting in degraded quality in image restoration. To simultaneously exploit the low-rank and JS priors, we propose a novel low-rank regularized joint sparsity model, dubbed LRJS, to enhance the dependency (i.e., low-rankness) of similar patches, thus better suppress independent noise. Moreover, to make the optimization tractable and robust, an alternating minimization algorithm with an adaptive parameter adjustment strategy is developed to solve the proposed LRJS-based image denoising problem. Experimental results demonstrate that the proposed LRJS outperforms many popular or state-of-the-art denoising algorithms in terms of both objective and visual perception metrics.

关键词： image denoising nonlocal sparse representation low-rank regularized joint sparsity alternating minimization adaptive parameter

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LABMAT: LEARNED FEATURE-DOMAIN BLOCK MATCHING FOR image RESTORATION

LABMAT: LEARNED FEATURE-DOMAIN BLOCK MATCHING FOR IMAGE REST...

引用

IEEE international conference on image processing (ICIP)

作者： Liang, Shijun Iskender, Berk Wen, Bihan Ravishankar, Saiprasad Michigan State Univ Dept Computat Math Sci & Engn E Lansing MI 48824 USA Michigan State Univ Dept Biomed Engn E Lansing MI 48824 USA Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781665441155

Grouping of similar patches, called block matching, has been widely used in image restoration applications. Popular block matching algorithms exploit image non-local similarities in spatial or a fixed transform domain, e.g., wavelets and DCT. However, applying these methods on corrupted patches usually leads to degraded matching accuracy, thus limiting the image restoration performance. In this work, we develop a novel methodology for performing block matching in a supervised way by learning multi-layer sparsifying transforms. The proposed learned transform-domain block matching method for image restoration, dubbed LABMAT, is shown to have better accuracy in terms of clustering similar blocks in the presence of noise, and it also achieves an improved denoising performance when it is incorporated into popular non-local denoising schemes.

关键词： Sparsifying transforms Sparse representations Block matching Denoising Transform learning

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A Parallel and Optimized image Compressed Sensing Solution 5

A Parallel and Optimized Image Compressed Sensing Solution

引用

5th IEEE international conference on Networking and advanced Systems, ICNAS 2021

作者： Bairi, Zakaria Bey, Kadda Beghdad Amamra, Abdennour Djamaa, Badis Ecole Militaire Polytechnique Algiers Algeria

ISBN: (纸本)9781665428217

Although there have been significant processor technology enhancements in terms of speed, data compression algorithms still do not accomplish the required task in a convenient time for voluminous data. The parallelism of the compression process could significantly improve not only the processing time but also the quality of the solution. In this paper, we propose a new Compressed Sensing (CS) solution based on Parallel CSNet (PCSNet) while integrating the PSNR loss to improve the image reconstruction process. The proposal results in distributing data to be processed to different cores of the machine and could benefit a multitude of applications including autonomous driving, medical imaging, and face detection. At its core, PCSNet uses a parallel convolutional neural network that includes a sampling subnetwork and a reconstruction subnetwork. These two networks learn the sampling matrix from the input image then the reconstructed image from the CS measurements. Thus, the training is done in a parallel way in each iteration then optimized at the master level using PSNR loss in an end-to-end learning process. Obtained experimental results outperform state-of-the-art approaches in terms of both image reconstruction quality and processing time. © 2021 IEEE.

关键词： Compressed sensing

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Offloading Deep Learning Empowered image Segmentation from UAV to Edge Server 44

Offloading Deep Learning Empowered Image Segmentation from U...

引用

44th international conference on Telecommunications and signal processing (TSP)

作者： Ilhan, Huseyin Enes Ozer, Sedat Kurt, Gunes Karabulut Cirpan, Hakan Ali Istanbul Tech Univ Dept Elect & Telecommun Engn Istanbul Turkey Bilkent Univ Dept Comp Sci Ankara Turkey Polytech Montreal Dept Elect Engn Polygrames Res Ctr Montreal PQ Canada

ISBN: (纸本)9781665429337

image and video analysis in unmanned aerial vehicle (UAV) systems have been a recent interest in many applications since the images taken by UAV systems can provide useful information in many domains including maintenance, surveillance and entertainment. However, a constraint on UAVs is having limited battery power and recent developments in the artificial intelligence (AI) domain encourages many applications to run computationally heavy algorithms on the taken UAV images. Such applications drain the power from the on-board battery rapidly, while requiring strong computationally strong resources. An alternative to that approach is offloading heavy tasks such as object segmentation to a remote (edge) server and perform the heavy computation on that server. However, the effect of the communication system and the used channel introduce noise on the transferred data and the effect of the noise due to the use of such LTE communication system on pre-trained deep networks has not been previously studied in the literature. In this paper, we study one such scenario where the images taken by UAVs and (the same images) transferred to an edge server via an LTE communication system under different scenarios. In our case, the edge server runs an off-the-shelf pretrained deep learning algorithm to segment the transmitted image. We provide an analysis of the effect of the wireless channel and the communication system on the final segmentation of the transmitted image on such a scenario.

关键词： Computational offloading image segmentation edge computing UAV image processing deep learning

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Fake currency detection with machine learning algorithm and image processing 5

Fake currency detection with machine learning algorithm and ...

引用

5th international conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Bhatia, Aman Kedia, Vansh Shroff, Anshul Kumar, Mayand Shah, Bickey Kumar Aryan Delhi Technological University Department of Software Engineering Delhi India Delhi Technological University Department of CSE Delhi India The University of Sheffield Department of Computer Science Sheffield South Yorkshire United Kingdom

ISBN: (纸本)9781665412728

This paper deals with the matter of identifying the currency that if the given sample of currency is fake. Different traditional strategies and methods are available for fake currency identification based on the colors, width, and serial numbers mentioned. In the advanced age of Computer science and high computational methods, various machine learning algorithms are proposed by image processing that gives 99.9% accuracy for the fake identity of the currency. Detection and recognition methods over the algorithms include entities like color, shape, paper width, image filtering on the note. This paper proposes a method for fake currency recognition using K-Nearest Neighbours followed by image processing. KNN has a high accuracy for small data sets making it desirable to be used for the computer vision task. In this, the banknote authentication dataset has been created with the high computational and mathematical strategies, which give the correct data and information regarding the entities and features related to the currency. Data processing and data Extraction is performed by implementing machine learning algorithms and image processing to acquire the final result and accuracy. © 2021 IEEE.

关键词： image processing

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Local Adaptive LMS Filtration of Multidimensional images 56

Local Adaptive LMS Filtration of Multidimensional Images

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56th international Scientific conference on Information, Communication and Energy Systems and Technologies (ICEST)

作者： Mironov, Rumen P. Draganov, Ivo R. Tech Univ Sofia Fac Telecommun Bulv Kl Ohridsky 8 Sofia 1000 Bulgaria

ISBN: (纸本)9781665428873

In this paper a new method for local adaptive three dimensional filtration of multidimensional halftone images is presented. The adaptation is based on the local information from the neighbourhood pixels of processed 3D image using generalized 3D LMS filter. An analysis of the quality of the processed multidimensional images is made on the basis of the calculated PSNR, SNR, MSE and the subjective observation. The given experimental results from the simulation in MATLAB environment for operating systems Windows of the developed filtration algorithms, based on a set of real test images are presented. By using the adaptive 3D LMS algorithm the average signal to noise ratio improvement is about 3 dB.

关键词： digital signal processing 3D image processing adaptive filtration LMS adaptation multidimensional image processing

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