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Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising 16

Joint Adaptive Sparsity and Low-Rankness on the Fly: An Onli...

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16th IEEE international Conference on Computer Vision (ICCV)

作者： Wen, Bihan Li, Yanjun Pfister, Luke Bresler, Yoram Univ Illinois Elect & Comp Engn & Coordinated Sci Lab Champaign IL 61820 USA

ISBN: (纸本)9781538610329

Recent works on adaptive sparse and low-rank signal modeling have demonstrated their usefulness, especially in image/video processing applications. While a patch-based sparse model imposes local structure, low-rankness of the grouped patches exploits non-local correlation. Applying either approach alone usually limits performance in various low-level vision tasks. In this work, we propose a novel video denoising method, based on an online tensor reconstruction scheme with a joint adaptive sparse and low-rank model, dubbed SALT. An efficient and unsupervised online unitary sparsifying transform learning method is introduced to impose adaptive sparsity on the fly. We develop an efficient 3D spatio-temporal data reconstruction framework based on the proposed online learning method, which exhibits low latency and can potentially handle streaming videos. To the best of our knowledge, this is the first work that combines adaptive sparsity and low-rankness for video denoising, and the first work of solving the proposed problem in an online fashion. We demonstrate video denoising results over commonly used videos from public datasets. Numerical experiments show that the proposed video denoising method outperforms competing methods.

关键词： Streaming media Noise reduction Transforms Tensile stress approximation algorithms image reconstruction Three-dimensional displays

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Segmentation and Tracking of Myocardial Boundaries Using Dynamic Programming 7th

Segmentation and Tracking of Myocardial Boundaries Using Dyn...

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7th international workshop on Statistical Atlases and Computational Models of the Heart (STACOM)

作者： Jacob, Athira J. Alex, Varghese Krishnamurthi, Ganapathy Indian Inst Technol Madras Tamil Nadu India

ISBN: (纸本)9783319527185;9783319527178

Increasing interest in quantification of local myocardial properties throughout the cardiac cycle from tagged MR (tMR) calls for treatment of the cardiac segmentation problem as a spatio-temporal task. The method presented for myocardial segmentation, uses dynamic programming to choose the optimal contour from a set of possible contours subject to maximizing a cost function. Robust Principle Component Analysis (RPCA)is used to decompose the time series into low rank and sparse components and initialization of the contour is done on the low rank approximation. The 3D nature of the images and tag grid location is incorporated into the cost function to get more robust results. 3D+t segmentation of patient data is achieved by propagating contours spatially and temporally. The method is ideal as a pre-processing step in motion quantification and strain rate mapping algorithms.

关键词： Dynamic programming Tagged MR image analysis Robust PCA Deformable contours Tracking 4D cardiac images Tag

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Structural damage identification using millimeter wave imaging and image processing 8

Structural damage identification using millimeter wave imagi...

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8th international Conference on Structural Health Monitoring of Intelligent Infrastructure, SHMII 2017

作者： Noori Hoshyar, A. Kharkovsky, S. Samali, B. Centre for Infrastructure Engineering SCEM Western Sydney University Australia

ISBN: (纸本)9781925553055

In the past decades, structural health monitoring (SHM) has received wide attention in preventing the sudden failure of structural components by identifying the damages in its early stages. Basically, to ensure the safety and reducing the serviceabili Otsu's thresholding ty of civil infrastructure it is important to inspect and assess the physical and functional condition of structures. Currently, manual inspection is the main form of assessing the conditions to ensure structure still meets the safety requirements. However, there are still several accidents that are reported as a result of insufficient inspection and conditional assessment of structures. In order to prevent further incidents, it is necessary to continuously inspect and assess the condition of structures with appropriate techniques. This is why the development and application of efficient non-destructive testing and computer vision methods for infrastructure health monitoring are in demand. This paper present the developed smart damage detection system for a local infrastructure health monitoring which complement the image-based damage detection methods in hazardous scenarios. The system is based on a relatively simple millimeter wave continuous wave reflectometer with an open-ended waveguide antenna for the purpose of automatic imaging of flaws such as cracks in a steel plate at different standoff distances, monitoring the crack to avoid further deformation through a sequence of inspections at intervals. However, in some cases the original images based on measured data do not provide desired information, leading to developing the image processing algorithm to enhance the imaging result. The proposed algorithm is based on Otsu's thresholding method and Prewitt approximation in six directions in an image which has been created from the measured data to facilitate the structural damage identification. The proposed algorithm can successfully enhance the quality of images and visualize the crack in a ste

关键词： nondestructive examination

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Proceedings of SPIE - The international Society for Optical Engineering

Proceedings of SPIE - The International Society for Optical ...

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2nd international workshop on Pattern Recognition, IWPR 2017

ISBN: (纸本)9781510613508

The proceedings contain 61 papers. The topics discussed include: a natural approach to convey numerical digits using hand activity recognition based on hand shape features;multiclass multiple kernel learning for HRRP-based radar target recognition;pattern recognition of concrete surface cracks and defects using integrated image processing algorithms;recognizing Chinese characters in digital ink from non-native language writers using hierarchical models;improved convolutional networks in forest species identification task;comparison expert and novice scan behavior for using e-learning;method for secure electronic voting system: face recognition based approach;application of OpenCV in Asus Tinker board for face recognition;upright detection of in-plain rotated face images with complicated background for organizing photos;carotid artery B-mode ultrasound image segmentation based on morphology, geometry and gradient direction;automated segmentation and isolation of touching cell nuclei in cytopathology smear images of pleural effusion using distance transform watershed method;automatic bone outer contour extraction from b-modes ultrasound images based on local phase symmetry and quadratic polynomial fitting;and facial fluid synthesis for assessment of acne vulgaris using luminescent visualization system through optical imaging and integration of fluorescent imaging system.

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A PULL-PUSH METHOD FOR FAST non-local MEANS FILTERING 23

A PULL-PUSH METHOD FOR FAST NON-LOCAL MEANS FILTERING

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23rd IEEE international Conference on image processing (ICIP)

作者： Isidoro, John R. Milanfar, Peyman Google Res Mountain View CA 94035 USA

ISBN: (纸本)9781467399616

non-local means filtering (NLM), has garnered a large amount of interest in the image processing community due to its capability to exploit image patch self-similarity in order to effectively filter noisy images. However, the computational complexity of non-local means filtering is the product of three different factors;namely, O (NDK), where K is the number of filter kernel taps (e.g. search window size), D is the number of patch taps, and N is number of pixels. We propose a fast approximation of non-local means filtering using the multiscale methodology of the pull-push scattered data interpolation method. By using NLM with a small filter kernel to selectively propagate filtering results and noise variance estimates from fine to coarse scales and back, the process can be used to provide comparable filtering capability to brute force NLM but with algorithmic complexity that is linear in the number of image pixels and the patch comparison taps, O(ND). In practical application, we demonstrate its denoising capability is comparable to NLM with much larger filter kernels, but at a fraction of the computational cost.

关键词： non-local means multiscale pull-push image denoising

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Accurate and High Throughput Cell Segmentation Method for Mouse Brain Nuclei Using Cascaded Convolutional Neural Network 3rd

Accurate and High Throughput Cell Segmentation Method for Mo...

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3rd international workshop on Patch-Based Techniques in Medical images (Patch-MI)

作者： Wang, Qian Wang, Shaoyu Zhu, Xiaofeng Liu, Tianyi Humphrey, Zachary Ghukasyan, Vladimir Conway, Mike Scott, Erik Fragola, Giulia Bradford, Kira Zylka, Mark J. Krishnamurthy, Ashok Stein, Jason L. Wu, Guorong Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian Shaanxi Peoples R China Univ N Carolina BRIC Chapel Hill NC 27515 USA Univ N Carolina Dept Radiol Chapel Hill NC 27599 USA Univ Penn Perelman Sch Med Philadelphia PA 19104 USA Univ N Carolina Neurosci Ctr Chapel Hill NC 27515 USA Renaissance Comp Inst RENCI Chapel Hill NC USA Univ N Carolina Dept Genet Chapel Hill NC 27515 USA

ISBN: (纸本)9783319674346;9783319674339

Recent innovations in tissue clearing and light sheet microscopy allow rapid acquisition of three-dimensional micron resolution images in fluorescently labeled brain samples. These data allow the observation of every cell in the brain, necessitating an accurate and high-throughput cell segmentation method in order to perform basic operations like counting number of cells within a region;however, large computational challenges given noise in the data and sheer number of features to identify. Inspired by the success of deep learning technique in medical imaging, we propose a supervised learning approach using convolution neural network (CNN) to learn the non-linear relationship between local image appearance (within an image patch) and manual segmentations (cell or background at the center of the underlying patch). In order to improve the segmentation accuracy, we further integrate high-level contextual features with low-level image appearance features. Specifically, we extract contextual features from the probability map of cells (output of current CNN) and train the next CNN based on both patch-wise image appearance and contextual features, extending previous methods into a cascaded approach. Using (a) high-level contextual features extracted from the cell probability map and (b) the spatial information of cell-to-cell locations, our cascaded CNN progressively improves the segmentation accuracy. We have evaluated the segmentation results on mouse brain images, and compared conventional image processing approaches. More accurate and robust segmentation results have been achieved with our cascaded CNN method, indicating the promising potential of our proposed cell segmentation method for use in large tissue cleared images.

关键词： Convolutional neural network Contextual feature Cascade learning Cell segmentation Mouse microscopy image

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The Application of non-orthogonal Multiple Access in Wireless Powered Communication Networks 17

The Application of Non-orthogonal Multiple Access in Wireles...

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17th IEEE international workshop on Signal processing Advances in Wireless Communications (SPAWC)

作者： Yuan, Yi Ding, Zhiguo Univ Lancaster Sch Comp & Commun Lancaster LA1 4YW England

ISBN: (纸本)9781509017492

This work investigates the application of non-orthogonal multiple access (NOMA) scheme for the uplink of wireless powered communication networks (WPCN). We maximize the sum rate by jointly designing of the time allocation, the downlink energy beamforming and receiver beamforming. To solve this nonconvex problem, a method of two stages optimization is proposed. In the first stage, we apply the series approximations to obtain the optimal energy beamforming and receive beamforming with fixed time allocation. In the second stage, one-dimensional search is considered to obtain the optimal time allocation. For the approximation part, successive convex approximation (SCA) method is used to convert a mixed integer non-linear program into its convex approximation problem. The numerical results illustrate that the proposed algorithm converges to the local optimum solution and the proposed scheme outperforms the fixed time allocation scheme and orthogonal multiple access (OMA).

关键词： Wireless power transfer non-orthogonal multiple access successive convex approximation

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Efficient Compressed Sensing Based MRI Reconstruction using nonconvex Total Variation Penalties 6

Efficient Compressed Sensing Based MRI Reconstruction using ...

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6th international workshop on New Computational Methods for Inverse Problems (NCMIP)

作者： Lazzaro, D. Piccolomini, E. Loli Zama, F. Univ Bologna Dept Math Bologna Italy

This work addresses the problem of Magnetic Resonance image Reconstruction from highly sub-sampled measurements in the Fourier domain. It is modeled as a constrained minimization problem, where the objective function is a non-convex function of the gradient of the unknown image and the constraints are given by the data fidelity term. We propose an algorithm, Fast non Convex Reweighted (FNCR), where the constrained problem is solved by a reweighting scheme, as a strategy to overcome the non-convexity of the objective function, with an adaptive adjustment of the penalization parameter. We propose a fast iterative algorithm and we can prove that it converges to a local minimum because the constrained problem satisfies the Kurdyka-Lojasiewicz property. Moreover the adaptation of non convex l0 approximation and penalization parameters, by means of a continuation technique, allows us to obtain good quality solutions, avoiding to get stuck in unwanted local minima. Some numerical experiments performed on MRI sub-sampled data show the efficiency of the algorithm and the accuracy of the solution.

关键词： image reconstruction

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ADAPTIVE REGULARIZER LEARNING FOR LOW RANK approximation WITH APPLICATION TO image DENOISING 23

ADAPTIVE REGULARIZER LEARNING FOR LOW RANK APPROXIMATION WIT...

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23rd IEEE international Conference on image processing (ICIP)

作者： Jia, Xixi Feng, Xiangchu Wang, Weiwei Xidian Univ Sch Math & Stat Xian Peoples R China

ISBN: (纸本)9781467399616

In this paper, we propose an adaptive regularizer learning method in the framework of MAP for low rank approximation (ARLLR). We assume that the prior distribution of the singular values is Laplacian with varying scale parameters. By using a full maximize a posterior (MAP) we learn the optimal scale parameters iteratively. We indicate that ARLLR is equivalent to low rank approximation regularized by Logarithm on singular values. In theory, we prove that ARLLR (Logarithm regularization) although being non-convex can be solved in closed form, and we further prove that local minimum can be easily obtained. Finally, ARLLR is applied to image de noising. Experimental results show that the proposed method enhances image denoising compared with state-of-the-art image denoising algorithms(especially for BM3D, SAIST and WNNM) in both quantity value (PSNR) and visual quality.

关键词： Low rank approximation Adaptive regularizer image denoising

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ROBUST MRI RECONSTRUCTION VIA RE-WEIGHTED TOTAL VARIATION AND non-local SPARSE REGRESSION 18

ROBUST MRI RECONSTRUCTION VIA RE-WEIGHTED TOTAL VARIATION AN...

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18th IEEE international workshop on Multimedia Signal processing (MMSP)

作者： Zhang, Mingli Desrosiers, Christian Ecole Technol Super Dept Software & IT Engn Montreal PQ Canada

ISBN: (纸本)9781509037247

Total variation (TV) based sparsity and nonlocal self-similarity have been shown to be powerful tools for the reconstruction of magnetic resonance (MR) images. However, due to the uniform regularization of gradient sparsity, standard TV approaches often over-smooth edges in the image, resulting in the loss of important details. This paper presents a novel compressed sensing method for the reconstruction of MRI data, which uses a regularization strategy based on reweighted TV to preserve image edges. This method also leverages the redundancy of nonlocal image patches through the use of a sparse regression model. An efficient strategy based on the Alternating Direction Method of Multipliers (ADMM) algorithm is used to recover images with the proposed model. Experimental results on a simulated phantom and real brain MR data show our method to outperform state-of-the-art compressed sensing approaches, by better preserving edges and removing artifacts in the image.

关键词： Compressed sensing Brain MRI Total Variation Re-weighted TV nonlocal similarity Sparse regression ADMM

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