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检索条件"主题词=Reconstruction algorithm"

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BM3D-AMP: A NEW IMAGE RECOVERY algorithm BASED ON BM3D DENOISING

BM3D-AMP: A NEW IMAGE RECOVERY ALGORITHM BASED ON BM3D DENOI...

引用

IEEE International Conference on Image Processing

作者： Christopher A. Metzler Arian Maleki Richard G. Baraniuk Department of Electrical and Computer Engineering at Rice University Department of Statistics at Columbia University

ISBN: (纸本)9781479983407

A denoising algorithm seeks to remove perturbations or errors from a signal. The last three decades have seen extensive research devoted to this arena, and as a result, today's denoisers are highly optimized algorithms that effectively remove large amounts of additive white Gaussian noise. A compressive sensing (CS) reconstruction algorithm seeks to recover a structured signal acquired from a small number of randomized measurements. Typical CS reconstruction algorithms can be cast as iteratively estimating a signal from a perturbed observation. This paper answers a natural question: How can one effectively employ a generic denoiser in a CS reconstruction algorithm? In response, we develop a denoising-based approximate message passing (D-AMP) algorithm that is capable of high-performance reconstruction. We demonstrate using the high performance BM3D denoiser that D-AMP offers state-of-the-art CS recovery performance for natural images (on average 9dB better than sparsity-based algorithms), while operating tens of times faster than the only competitive method. A critical insight in our approach is the use of an appropriate Onsager correction term in the D-AMP iterations, which coerces the signal perturbation at each iteration to be very close to the white Gaussian noise that denoisers are typically designed to remove. On the analytical side, we develop a new state evolution framework for deterministic signals that accurately predicts the performance of D-AMP and enables us to derive several useful theoretical features.

关键词： Compressive Sensing Denoising Approximate Message Passing Onsager Compressed sensing reconstruction algorithm Noise reduction carbon monosulfide CONTAINMENT SPRAY Cesium images iterative methods Gaussian white noise algorithms conditioned stimulus perturbation signals AWGN

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FPA-CS: Focal Plane Array-based Compressive Imaging in Short-wave Infrared

FPA-CS: Focal Plane Array-based Compressive Imaging in Short...

引用

IEEE Conference on Computer Vision and Pattern Recognition

作者： Huaijin Chen M. Salman Asif Aswin C. Sankaranarayanan Ashok Veeraraghavan ECE Department Rice University ECE Department Carnegie Mellon University

ISBN: (纸本)9781467369657

Cameras for imaging in short and mid-wave infrared spectra are significantly more expensive than their counterparts in visible imaging. As a result, high-resolution imaging in those spectrum remains beyond the reach of most consumers. Over the last decade, compressive sensing (CS) has emerged as a potential means to realize inexpensive short-wave infrared cameras. One approach for doing this is the single-pixel camera (SPC) where a single detector acquires coded measurements of a high-resolution image. A computational reconstruction algorithm is then used to recover the image from these coded measurements. Unfortunately, the measurement rate of a SPC is insufficient to enable imaging at high spatial and temporal resolutions. We present a focal plane array-based compressive sensing (FPA-CS) architecture that achieves high spatial and temporal resolutions. The idea is to use an array of SPCs that sense in parallel to increase the measurement rate, and consequently, the achievable spatio-temporal resolution of the camera. We develop a proof-of-concept prototype in the short-wave infrared using a sensor with 64×64 pixels;the prototype provides a 4096× increase in the measurement rate compared to the SPC and achieves a megapixel resolution at video rate using CS techniques.

关键词： Short Waves statistical process control UCN3 gene SUPPRESSION POOL COOLING Cameras short wave infrared temporal resolution Imaging Compressed sensing reconstruction algorithm plane Platanus hybrida Planing Infrared radiation

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CONVEX CLUSTERING AND RECOVERY OF PARTIALLY OBSERVED DATA

CONVEX CLUSTERING AND RECOVERY OF PARTIALLY OBSERVED DATA

引用

IEEE International Conference on Image Processing

作者： Sunrita Poddar Mathews Jacob Department of Electrical and Computer Engineering The University of Iowa

ISBN: (纸本)9781467399623

We propose a convex clustering and reconstruction algorithm for data with missing entries. The algorithm uses a similarity measure between every pair of points to cluster and recover the data. The cluster centres can be recovered reliably when the ground-truth similarity matrix is available. Moreover, the similarity matrix can also be reliably estimated from the partially observed data, when the clusters are well-separated and the coherence of the difference between points from different clusters is low. The algorithm performs well using the estimated similarity matrix on a simulated dataset. The method is also successful in reconstructing images from under-sampled Fourier data.

关键词： Clustering Missing Data Image reconstruction Matrix Completion missing data image reconstruction similarity matrix Cluster Analysis similar measure reconstruction algorithm

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Diagnostics of the BIOMASS Feed Array Prototype

Diagnostics of the BIOMASS Feed Array Prototype

引用

European Conference on Antennas and Propagation

作者： C. Cappellin S. Pivnenko K. Pontoppidan E. Jorgensen P. Meincke TICRA Copenhagen Denmark Department of Electrical Engineering Technical University of Denmark Kgs. Lyngby Denmark

ISBN: (纸本)9781467321877

The 3D reconstruction algorithm is applied to the prototype feed array of the BIOMASS synthetic aperture radar, recently measured at the DTU-ESA Spherical Near-Field Antenna Test Facility in Denmark. Careful analysis of the measured feed array data has shown that the test support structure of the array has a dominant influence on the measured feed pattern. The 3D reconstruction is then applied to the feed array measured data, allowing the identification of the undesired currents induced on the feed support structure. The results of the diagnostics and the corresponding improvements of the feed pattern obtained by removal of the undesired currents are presented and discussed.

关键词： three-dimensional displays FEEDS Diagnosis Biomass supporting structure arrays reconstruction algorithm

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DIRECT INVERSION OF MOJETTE PROJECTIONS

DIRECT INVERSION OF MOJETTE PROJECTIONS

引用

IEEE International Conference on Image Processing

作者： Imants Svalbe Andrew Kingston Jeanpierre Guédon Nicolas Normand Shekhar Chandra School of Physics Monash University AUS. Dept. Applied Math. RSPE Australian National University AUS. LUNAM Université Université de Nantes IRCCyN UMR CNRS FRA. Information and Communication Technologies CSIRO AUS.

ISBN: (纸本)9781479923427

We present algorithms to reconstruct images from minimal sets of discrete Mojette projections using direct back-projection (DBP) with various forms of correction. This paper extends previous work on discrete projection inversion by Servières et al [1, 2, 3]. The number of Mojette projections needed for exact inversion by DBP (EI-DBP) scales as O(N~2). A new form of discrete interpolation is developed to expand the point spread function (PSF) of a minimal (Katz-sufficient) set of discrete projections to encompass new directions and thus augment the size of the reconstruction region to which EI-DBP applies. Additionally, we propose a Fourier domain filter for Mojette back-projection that is built from the discrete PSF of the given Mojette angle set and the autocorrelation function of the image domain. These discrete reconstruction methods are targeted for use with noisy sets of real projection data.

关键词： discrete tomography reconstruction projection back-projection Mojette transforms discrete Radon transforms PROJECTION backprojection discrete transforms Autocorrelation function Point spread function Inversion Minimal Set reconstructions reconstruction algorithm Reconstructive Surgical Procedures Projection discrete element

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Image Shadow Removal Based on Residual Neural Network

Image Shadow Removal Based on Residual Neural Network

引用

International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Wei Zheng Xiuping Teng School of Information and Electronic Engineering Shandong Institute of Business and Technology Yantai China

ISBN: (纸本)9781728105529

The removal of image shadows has always been a challenging task that requires us to detect shadows and understand the surrounding scenes. The existing method of shadow detection and removal first locates the shadow area by shadow detection,and then uses some reconstruction algorithms to remove the shadows of the umbra and penumbra. However, detecting shadows is already a very rare task. Based on the traditional physical methods can be applied to a high quality image, and a method based on statistical characteristics must manually tag shadow. In this paper,we use a convolutional residual neural network to train the model. Using the residual neural network, we can prevent degradation due to the excessive number of network layers. The trained model can detect the shadow area by inputting the global image and combining the semantics of the picture. In these two aspects, good shadow area detection and positioning can be obtained, and image shadow removal can be realized.

关键词： Shadow-removal ResNet CNN shadows reconstruction algorithm Imagery (Psychotherapy) images Image quality umbras Image Neural network network layer

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A GPU Accelerated algorithm for Compressive sensing based Video Super-resolution

A GPU Accelerated Algorithm for Compressive sensing based Vi...

引用

International Conference on Audio, Language and Image Processing

作者： Xifei Wu Hui Xiang School of Computer Science and Technology Shandong University

ISBN: (纸本)9781467301732

This paper presents a parallel algorithm designed for video reconstruction based on Compressive sensing on the platform of GPU. The reconstruction algorithm based on compressive sensing can achieve a good performance than traditional algorithm, but the process is more complex. With the aid of the GPU acceleration and the redundancy calculation between the adjacent frames, we can achieve real-time video reconstruction result. During the process of acceleration, we divided the whole process into four stages, and find that all the stages are suit for parallel computing. Compared to the sequential algorithm, the parallel algorithm achieved a speed up of 35 times. Excepted for GPU acceleration, some other methods to reduce computation of reconstruction for video-frame is proposed. At last, the result of the parallel algorithm is shown and analyzed.

关键词： Compressed sensing Parallel algorithms GRAPPER PICK UP Graphics Processing Unit acceleration reconstruction algorithm PARALLEL PROCESSING (COMPUTERS) algorithms super-resolution Videotapes

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NEURAL NETWOK BASED X-RAY TOMOGRAPHY FOR FAST INSPECTION OF APPLES ON A CONVEYOR BELT SYSTEM

NEURAL NETWOK BASED X-RAY TOMOGRAPHY FOR FAST INSPECTION OF ...

引用

IEEE International Conference on Image Processing

作者： Eline Janssens Jan De Beenhouwer Mattias Van Dael Pieter Verboven Bart Nicolai Jan Sijbers iMinds - Vision Lab University of Antwerp (CDE) BIOSYST-MeBios KU Leuven

ISBN: (纸本)9781479983407

The throughput of an inline computed tomography (CT) based inspection system depends on the speed of its image reconstruction algorithm. Filtered back projection (FBP) provides fast reconstructions, but requires many high quality radiographs from all angles to obtain accurate reconstructions. This is not achievable in an inline environment. Iterative reconstruction methods yield adequate reconstructions from limited, but they are slow. Recently a new reconstruction algorithm was introduced [1] that can handle limited data and is very fast: the neural network FBP (NN-FBP). In this work, we introduce a neural network (NN) based Hilbert transform FBP (NN-hFBP) for inline inspection. This method reconstructs images with a filter-based Hilbert transform FBP method. The filters are application specific and trained by a neural network. Comparison of the NN-hFBP and conventional reconstruction methods applied to inline fan-beam X-ray data of apples shows that the NN-hFBP yields high quality images in a short reconstruction time.

关键词： Inline tomography Artificial neural networks Filtered backprojection filtered backprojection reconstruction algorithm Tomography, X-Ray Crab Apple Artificial neural networks Neural network Belt conveyors Reconstructive Surgical Procedures Custom design Image quality Inspection Throughput

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LOSSLESS COMPRESSION OF BAYER MASK IMAGES USING AN OPTIMAL VECTOR PREDICTION TECHNIQUE

LOSSLESS COMPRESSION OF BAYER MASK IMAGES USING AN OPTIMAL V...

引用

European Signal Processing Conference

作者： Stefano Andriani Giancarlo Calvagno Daniele Menon University of Pisa

In this paper a lossless compression technique for Bayer pattern images is presented. The common way to save these images was to colour reconstruct them and then code the full resolution images using one of the lossless or lossy methods. This solution is useful to show the captured images at once, but it is not convenient for efficient source coding. In fact, the resulting full colour image is three times greater than the Bayer pattern image and the compression algorithms are not able to remove the correlations introduced by the reconstruction algorithm. However, the Bayer pattern images present new problems for the coding step. In fact, adjacent pixels belong to different colour bands mixing up different kinds of correlations. In this paper we present a lossless compression procedure based on an optimal vector predictor, where the Bayer pattern is divided into non-overlapped 2×2 blocks, each of them predicted as a vector. We show that this solution is able to exploit the existing correlation giving a good improvement of the compression ratio with respect to other lossless compression techniques, e.g., JPEG-LS.

关键词： Bayer pattern lossless compression compression ratio Compression algorithms source coding reconstruction algorithm Lossless

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COMPRESSIVE GAUSSIAN MIXTURE ESTIMATION

COMPRESSIVE GAUSSIAN MIXTURE ESTIMATION

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Anthony Bourrier Remi Gribonval Patrick Perez Technicolor INRIA Rennes-Bretagne Atlantique

ISBN: (纸本)9781479903573

When fitting a probability model to voluminous data, memory and computational time can become prohibitive. In this paper, we propose a framework aimed at fitting a mixture of isotropic Gaussians to data vectors by computing a low-dimensional sketch of the data. The sketch represents empirical moments of the underlying probability distribution. Deriving a reconstruction algorithm by analogy with compressive sensing, we experimentally show that it is possible to precisely estimate the mixture parameters provided that the sketch is large enough. Our algorithm provides good reconstruction and scales to higher dimensions than previous probability mixture estimation algorithms, while consuming less memory in the case of numerous data. It also provides a privacy-preserving data analysis tool, since the sketch doesn't disclose information about individual datum it is based on.

关键词： Gaussian mixture estimation Compressive sensing Database sketch Compressive learning Compressed sensing Sketches Gauss reconstruction algorithm Data storage devices Probability distribution computational time Data analysis

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