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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101-110 订阅

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SEMANTIC SEGMENTATION IN DEPTH DATA : A COMPARATIVE EVALUATION OF image AND POINT CLOUD BASED METHODS

SEMANTIC SEGMENTATION IN DEPTH DATA : A COMPARATIVE EVALUATI...

引用

IEEE international conference on image processing (ICIP)

作者： Katrolia, Jigyasa Singh Kraemer, Lars Rambach, Jason Mirbach, Bruno Stricker, Didier DFKI Kaiserslautern Germany Tech Univ Kaiserslautern Kaiserslautern Germany

ISBN: (纸本)9781665441155

The problem of semantic segmentation from depth images can be addressed by segmenting directly in the image domain or at 3D point cloud level. In this paper, we attempt for the first time to provide a study and experimental comparison of the two approaches. Through experiments on three datasets, namely SUN RGB-D, NYUdV2 and TICaM, we extensively compare various semantic segmentation algorithms, the input to which includes images and point clouds derived from them. Based on this, we offer analysis of the performance and computational cost of these algorithms that can provide guidelines on when each method should be preferred.

关键词： scene segmentation depth image point cloud

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A DUAL DOMAIN NETWORK FOR MRI RECONSTRUCTION USING GABOR LOSS

A DUAL DOMAIN NETWORK FOR MRI RECONSTRUCTION USING GABOR LOS...

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IEEE international conference on image processing (ICIP)

作者： Seo, Hyunseok Shin, Kelly M. Kyung, Yeunwoong KIST Ctr Bion Seoul South Korea CheongShim Int Acad Gapyeong South Korea Hanshin Univ Sch Comp Engn Osan South Korea

ISBN: (纸本)9781665441155

Fast magnetic resonance imaging (MRI) scan is usually achieved by undersampling in k-space, and reconstruction methods for image domain is indispensable. Conventional reconstruction methods rely on independent sensitivity maps of the receiver coils to synthesize the components of the spatial harmonics in k-space. In recent years, deep learning-based MRI algorithms have been providing more accurate reconstructed image than the traditional results. Nonetheless, there is room for improvement. In this study, we proposed a new deep learning-based reconstruction algorithm to use image and k-space domain data simultaneously. The Gabor filter was also defined to effectively incorporate two different domain data in learning stage. Experimental results using real MRI data showed that the proposed method outperforms other deep learning-based algorithms for three metrics of nMSE, SSIM, and VIF.

关键词： Deep learning Gabor filter image reconstruction MRI Neural networks

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LEARNING SPARSIFYING TRANSFORMS FOR image RECONSTRUCTION IN ELECTRICAL IMPEDANCE TOMOGRAPHY

LEARNING SPARSIFYING TRANSFORMS FOR IMAGE RECONSTRUCTION IN ...

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

作者： Yang, Kaiyi Borijindargoon, Narong Ng, Boon Poh Ravishankar, Saiprasad Wen, Bihan Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Michigan State Univ Dept Computat Math Sci & Engn E Lansing MI 48824 USA

ISBN: (纸本)9781728176055

Electrical Impedance Tomography (EIT) is a fast and non-invasive imaging technology that reconstructs the internal electrical properties of a subject. However, its functionality is limited by low spatial resolution arising from an ill-posed and ill-conditioned inverse problem. Several sparsity-promoting regularization methods have been applied to improve the quality of EIT image reconstruction, including various l(0) and l(1)-based analytical models (TV, TwIST, etc.), and a patch-based sparse representation via a learned dictionary (using the K-SVD algorithm), dubbed CS-EIT. To further exploit the potential of compressed sensing in Electrical Impedance Tomography, this paper incorporates the recent novel method of transform learning for EIT image reconstruction. We propose a blind compressed sensing algorithm, dubbed TL-EIT, which simultaneously optimizes the sparsifying transform and updates the reconstructed image. We demonstrate using both synthetic and in vivo data that the proposed TL-EIT is more effective than other sparsity-based algorithms for reconstructing high-quality EIT images. In addition, TL-EIT also accelerates the reconstruction process in comparison to other learning-based algorithms like CS-EIT.

关键词： Sparsifying transforms Electrical impedance tomography Inverse problems image reconstruction Sparse representation

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SELF-CONVOLUTION: A HIGHLY-EFFICIENT OPERATOR FOR NON-LOCAL image RESTORATION

SELF-CONVOLUTION: A HIGHLY-EFFICIENT OPERATOR FOR NON-LOCAL ...

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

作者： Guo, Lanqing Zha, Zhiyuan Ravishankar, Saiprasad Wen, Bihan Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore Michigan State Univ Dept Computat Math Sci & Engn E Lansing MI 48824 USA Michigan State Univ Dept Biomed Engn E Lansing MI 48824 USA

ISBN: (纸本)9781728176055

Constructing effective image priors is critical to solving ill-posed inverse problems, such as image restoration. Recent works proposed to exploit image non-local similarity for inverse problems by grouping similar patches, and demonstrated state-of-the-art results in many applications. However, comparing to classic local methods based on filtering or sparsity, most of the non-local algorithms are time-consuming, mainly due to the highly inefficient and redundant block matching step, where the distance between each pair of overlapping patches needs to be computed. In this work, we propose a novel Self-Convolution operator to exploit image non-local similarity in a self-supervised way. The proposed Self-Convolution can generalize the commonly-used block matching step, and produce the equivalent results with much cheaper computation. Based on Self-Convolution, we propose an effective multi-modality image restoration scheme, which is much more efficient than conventional block matching for non-local modeling. Experimental results also demonstrate that Self-Convolution can significantly speed up most of the popular non-local image restoration algorithms, with two-fold to nine-fold faster block matching. The codes will be released on GitHub.

关键词： image restoration Block matching Fast Fourier Transform Convolution Non-local similarity

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CRYO-ELECTRON MICROSCOPY image DENOISING USING MULTI-FREQUENCY VECTOR DIFFUSION MAPS

CRYO-ELECTRON MICROSCOPY IMAGE DENOISING USING MULTI-FREQUEN...

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IEEE international conference on image processing (ICIP)

作者： Fan, Yifeng Zhao, Zhizhen Univ Illinois Urbana Champaign Dept ECE Champaign IL 61820 USA Univ Illinois Urbana Champaign Coordinated Sci Lab Champaign IL 61820 USA

ISBN: (纸本)9781665441155

Cryo-electron microscopy (EM) single particle reconstruction is a general technique for 3D structure determination of macromolecules. However, because the images are taken at low electron dose, it is extremely hard to visualize the individual particle with low contrast and high noise level. In this paper, we propose a novel framework for cryo-EM single particle image denoising, which incorporates the recently developed multi-frequency vector diffusion maps [1] for improving the identification and alignment of images with similar viewing directions. In addition, we propose a novel filtering scheme combining graph signal processing and truncated Fourier-Bessel expansion of the projection images. Through both simulated and publicly available real data, we demonstrate that our proposed method is efficient and robust to noise compared with the state-of-the-art cryo-EM 2D class averaging algorithms.

关键词： Cryo-electron microscopy multi-frequency vector diffusion maps image denoising

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Deep Learning Solution for Diabetic Retinopathy Diagnosis based on Convolutional Neural Networks and image processing algorithms 9

Deep Learning Solution for Diabetic Retinopathy Diagnosis ba...

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9th IEEE international conference on e-Health and Bioengineering (EHB)

作者： Ion, Tomozei Cosmin Elena, Nechita Lazar, Dorian Vasile Alecsandri Univ Bacau Dept Math & Informat Bacau Romania Univ Bucharest Bucharest Romania

ISBN: (纸本)9781665440004

The purpose of this paper is to reflect upon the results obtained for diabetic retinopathy diagnosis through the implementation of high-performance computing algorithms, libraries and programming languages. Their subsequent implementations aim to help medical practitioners in a precise determination of symptoms related to diabetic retinopathy, with minimal errors and with the support of Machine Learning and image processing algorithms. The solution was developed based on Python and the NumPy, Pandas, Tensorflow, Keras and Pillow libraries. The correctness of the diagnosis varies from 70% to 77%.

关键词： Diabetic Retinopathy Machine Learning image processing algorithms Convolutional Neural Network Inception Network

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Vessel image Enhancement Method Using Nonlinear Transformation Based on Guided Filtering

Vessel Image Enhancement Method Using Nonlinear Transformati...

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2021 IEEE international conference on signal processing, Communications and Computing, ICSPCC 2021

作者： Qi, Tang Wen-Tian, Wang Ke-Xin, Zhou Yi-Xuan, Sun Xi'An University of Posts Telecommunication Xi'an China

ISBN: (纸本)9781665429184

Aiming at the problems of blurred contrast and difficult diagnosis of vascular tissue in medical images, A nonlinear transform blood vessel enhancement method based on guided filtering is proposed. In this paper, a hybrid filter is obtained by fusing guided filtering and Laplacian filtering to complete the preprocessing of image vessel detail enhancement, then the green component is stretched by nonlinear mapping function in RGB color space, the red component and blue component are adjusted according to the stretching degree. Finally, the saturation component is nonlinear contrast stretched in HSV color space to achieve vascular image enhancement. The paper uses objective evaluation methods such as image DV-BV, information entropy and average gradient to compare the performance of existing image enhancement algorithms. The results show that the proposed method has excellent performance. © 2021 IEEE.

关键词： image enhancement

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CONVERGENT PLUG-AND-PLAY USING CONTRACTIVE DENOISERS 49

CONVERGENT PLUG-AND-PLAY USING CONTRACTIVE DENOISERS

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

作者： Nair, Pravin Chaudhury, Kunal N. Samsung Res Inst Bengaluru India Indian Inst Sci Bengaluru India

ISBN: (纸本)9798350344868;9798350344851

Plug-and-Play (PnP) algorithms leverage the power of modern denoisers for image reconstruction. They have been shown to deliver state-of-the-art reconstructions using CNN denoisers. It was established in recent works that convergence of these iterative algorithms can be guaranteed using nonexpansive denoisers. However, integrating nonexpansivity into gradient-based learning is challenging. Existing algorithms for training nonexpansive denoisers often cannot guarantee nonexpansivity or are computationally intensive. The present work is based on the observation that the convergence of PnP-FBS and PnP-BBS (PnP based on Forward-Backward and Backward-Backward Splittings) can be guaranteed using contractive denoisers. In this regard, we show that by unfolding FBS iterations applied to wavelet denoising, we can construct contractive image denoisers whose regularization capacity is comparable to CNN denoisers. To the best of knowledge, this is the first work to introduce a simple framework for training denoisers that are provably contractive.

关键词： image reconstruction plug-and-play method denoiser contraction operator unrolling

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An Optimization Method for Video Upsampling and Downsampling Using Interpolation-Dependent image Downsampling 4

An Optimization Method for Video Upsampling and Downsampling...

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

作者： Yang, Ding Jiabao, Bai School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781665407571

In this paper, an optimization method is proposed by analyzing interpolation-dependent image downsampling (IDID) and bicubic interpolation algorithms to improve the quality of video upsampling and downsampling. The bicubic algorithm is used as a reference to find the optimal downsampled image by setting its mapping relationship to geometric center alignment and improving bicubic for the interpolation kernel coefficients. The Peak signal to Noise Ratio(PSNR) of the optimized algorithm is improved by 0.64 dB and 0.14 dB on average over bicubic and IDID_cubic, respectively. To verify the effectiveness of this scheme, the upsampling and downsampling effects of bicubic and bicubic of interpolation-dependent image downsampling (IDID_bicubic) in video sequences are compared and analyzed. The results show that the optimized algorithm improves the video image quality, which is important for the application to improve the overall quality of video codec. © 2021 IEEE.

关键词： Interpolation

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Aspects of signal processing for Multistatic Terahertz Imaging Systems 46

Aspects of Signal Processing for Multistatic Terahertz Imagi...

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46th international conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz)

作者： Hussung, Raphael Keil, Andreas Kahl, Matthias Souliman, Aya Weisenstein, Christian Bolivar, Peter Haring Friederich, Fabian Fraunhofer Inst Ind Math ITWM Kaiserslautern Germany Becker Photon GmbH Porta Westfalica Germany Univ Siegen High Frequency & Quantum Elect Siegen Germany

ISBN: (纸本)9781728194240

Applications of multistatic sensor arrays, which operate in the terahertz-region, are increasingly gaining importance for non-destructive testing purposes. Continuous enhancements in processing capabilities of CPUs and GPUs allow to generate volumetric images with superior speeds compared to alternative raster-scan systems. However, this requires efficient reconstruction-and correction algorithms. In addition to the system design, the choice of signal processing methods has a decisive influence on the resolution, image quality and measurement duration. In this contribution, we discuss some aspects of this signal processing along with specific examples.

关键词： image quality image resolution signal processing algorithms Imaging System analysis and design signal resolution image reconstruction

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