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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data IV"

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Advanced algorithms for identifying targets from a three-dimensional reconstruction of sparse 3D Ladar data

Advanced algorithms for identifying targets from a three-dim...

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conference on Optical Complex Systems (OCS)

作者： Berechet, Ion Berginc, Gerard SISPIA SARL 18 Allee Henri Dunant F-94300 Vincennes France THALES OPTRONIQUE F-94300 Vincennes France

ISBN: (纸本)9780819487988

There is a considerable interest in the development of new optical imaging systems that are able to give three-dimensional images. Potential applications range across medical imaging, surveillance and robotic vision. Identifying targets or objects concealed by foliage or camouflage is a critical requirement for operations in public safety, law enforcement and defense. The most promising techniques for these tasks are 3D laser imaging techniques. Their principles are to use movable light sources and detectors to collect information on laser scattering and to reconstruct the 3D objects of interest. 3D reconstruction algorithm is a major component in these optical systems for identification of camouflaged objects. But 3D reconstruction must take into account sparse collected data i.e. concealed objects and reconstruction algorithms must solve a complex multi-parameter inverse problem. Therefore the inverse problem of recovering the surface three-dimensional shape function from intensity data is more challenging. The objective of our paper is to present a new algorithmic approach for the generation of 3D surface data from 3D point clouds corresponding to reconstruction algorithm. This algorithmic approach is based on research of automatic minimization of an energy function associated with a sparse structure of 3D points. The role of this type of algorithmic data-driving process is to complete the incomplete 3D image at satisfactory levels for reliable identification of concealed objects.

关键词： 3D Ladar foliage penetration data-driving process energy minimum incomplete area completion point cloud surface generation

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A novel hybrid compressed sensing image reconstrcution method

A novel hybrid compressed sensing image reconstrcution metho...

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International conference on Electronics and Optoelectronics

作者： Wang, Shanshan Liu, Qiegen Luo, Jianhua Zhu, Yuemin College of Life Science and Technology Shanghai Jiaotong University Shanghai China CREATIS CNRS UMR 5220 University of Lyon Villeurbanne France

ISBN: (纸本)9781612842738

In this paper, a hybrid approach of image reconstruction from highly incomplete data is introduced. The method is a weighted recursive filtering procedure. At each iteration, random noise is first injected in the unknown portion of the spectrum, and then a reweighted denoising filter consisting of Block Matching 3D (BM3D) filter and multiscale L0-continuation filter is exploited to attenuate the noise in the image domain and reveal new features and details, finally those new features are projected onto the unknown portion of the spectrum to update the K-space data. The proposed method avoids local solutions and recovers the features and details of the image efficiently by utilizing advantages of both filters. The experimental results on both simulated and real images consistently demonstrate that the proposed approach can efficiently reconstruct the image with high image quality. © 2011 IEEE.

关键词： image denoising

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Projective reconstruction from incomplete trajectories by global and local constraints

Projective reconstruction from incomplete trajectories by gl...

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8th European conference on Visual Media Production, CVMP 2011

作者： Ackermann, H. Rosenhahn, B. Leibniz University Hanover Germany

ISBN: (纸本)9780769546216

The paper deals with projective shape and motion reconstruction by subspace iterations. A prerequisite of factorization-style algorithms is that all feature points need be observed in all images, a condition which is hardly realistic in real videos. We therefore address the problem of estimating structure and motion considering missing features. The proposed algorithm does not require initialization and uniformly handles all available data. The computed solution is global in the sense that it does not merge partial solutions incrementally or hierarchically. The global cost due to the factorization is further amended by local constraints to regularize and stabilize the estimations. It is shown how both costs can be jointly minimized in the presence of unobserved points. By synthetic and real image sequences with up to 60% missing data we demonstrate that our algorithm is accurate and reliable. © 2011 IEEE.

关键词： Factorization

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On numerical simulation of high-speed CCD/CMOS-based wavefront sensors for adaptive optics

On numerical simulation of high-speed CCD/CMOS-based wavefro...

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conference on Astronomical Adaptive Optics Systems and Applications iv

作者： Konnik, Mikhail V. Welsh, James Stuart Univ Newcastle Sch Elect Engn & Comp Sci Callaghan NSW 2308 Australia

ISBN: (纸本)9780819487599

Wavefront sensors, which use solid-state CCD or CMOS photosensors, are sources of errors in adaptive optic systems. Inaccuracy in the detection of wavefront distortions introduces considerable errors into wavefront reconstruction and leads to overall performance degradation of the adaptive optics system. The accuracy of wavefront sensors is significantly affected by photosensor noise. Thus, it is crucial to formulate high-level photosensor models that enable adaptive optic engineers to simulate realistic effects of noise from wavefront sensors. However, the complexity of solid-state photosensors and multiple noise sources makes it difficult to formulate an adequate model of the photosensor. Moreover, the characterisation of the simulated sensor and comparison with real hardware is often incomplete due to lack of comprehensive standards and guidelines. Owe to these difficulties, engineers work with oversimplified models of the wavefront sensors and consequently have imprecise numerical simulation results. The paper presents an approach for the modelling of noise sources for CCD and CMOS sensors that are used for wavefront sensing in adaptive optics. Both dark and light noise such as fixed pattern noise, photon shot noise, and read noises, as well as, charge-to-voltage noises are described. Procedures for characterisation of both light and dark noises of the simulated photosensors are provided. Numerical simulation results of a photosensor for a high-frame rate Shack-Hartmann wavefront sensor are presented.

关键词： Interference (communication) Wavefront sensors Signal to noise ratio Sensors Charge-coupled devices CMOS sensors Adaptive optics CCD image sensors Numerical simulations Electrons

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Evaluation of 2D image reconstruction using Fan-Beam FBP Algorithm with no Backprojection Weight from WLP Completed Truncated Projection data

Evaluation of 2D Image Reconstruction using Fan-Beam FBP Alg...

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2011 4th International conference on Biomedical Engineering and Informatics(第四届生物医学工程与信息学国际会议 BMEI 2011)

作者： K. Sumith A. V. Narasimhadhan Kasi Rajgopal Dept. Of Electrical Engg Indian Institute of ScienceBangaloreIndia

This paper presents the image reconstruction using the fan-beam filtered backprojection (FBP) algorithm with no backprojection weight from windowed linear prediction (WLP) completed truncated projection data. The image reconstruction from truncated projections aims to reconstruct the object accurately from the available limited projection data. Due to the incomplete projection data, the reconstructed image contains truncation artifacts which extends into the region of interest (ROI) making the reconstructed image unsuitable for further use. data completion techniques have been shown to be effective in such situations. We use windowed linear prediction technique for projection completion and then use the fan-beam FBP algorithm with no backprojection weight for the 2-D image reconstruction. We evaluate the quality of the reconstructed image using fan-beam FBP algorithm with no backprojection weight after WLP completion.

关键词： Fan-beam algorithm truncated data Windowed Linear Prediction.

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Sequential regularized MLEM reconstruction method for incomplete sinograms

Sequential regularized MLEM reconstruction method for incomp...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Uygar Tuna Antonietta Pepe Ulla Ruotsalainen Department of Signal Processing BioMediTech Tampere University of Technology Tampere Finland

In this study, we investigate the capability of the well-known maximum likelihood expectation maximization (MLEM) method in handling the incomplete sinogram reconstruction. MLEM method is known to be able to deal with the missing parts via system matrix modeling. We propose sequentially applied regularized MLEM method for the image reconstruction from the incomplete sinograms. Median filtering (MED), L-filtering (L) and block matching 3D (BM3D) filtering were employed as spatial domain regularizers, separately. The MLEM method was applied sequentially by decreasing gradually the amount of the regularization when the image update between the consecutive iterations was negligible. The sequential application of the MLEM method is used to compensate for the blurring caused by the regularization filters. In order to test the proposed approach thoroughly, we constructed wide test dataset consisting of three sparse sinograms (by using 25%, 10% and 5.5% of the sinogram data), the ECAT HRRT type sinogram (81.2% of the sinogram data), ClearPET type sinogram (88.3% of the sinogram data), AX-PET type sinogram (56% of the sinogram data) and a hypothetical checkerboard type sinogram (50% of the sinogram data) of the Shepp-Logan phantom. The constructed incomplete sinograms were contaminated with the Poisson noise. The incomplete sinograms were reconstructed by using filtered backprojection, MLEM without filtering, MLEM with MED, MLEM with L and MLEM with BM3D filtering. We evaluated the approach visually and with the quantitative normalized mean square error (NMSE). The results showed that MLEM without filtering cannot correct the artifacts related to the missing sinogram bins for some of the generated incomplete sinograms. On the other hand, MLEM method with spatial regularization filter was able to provide successfully reconstructed images for all cases. Quantitative NMSE values were in parallel with the visual impression of the reconstructed images.

关键词： Information filters Biological system modeling Noise

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Projective reconstruction from incomplete Trajectories by Global and Local Constraints

Projective Reconstruction from Incomplete Trajectories by Gl...

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conference for Visual Media Production (CVMP)

作者： H. Ackermann B. Rosenhahn Leibniz University Hanover Germany

关键词： Vectors Cameras Trajectory Geometry Estimation image reconstruction Subspace constraints

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Compounded Regularization and Fast Algorithm for Compressive Sensing Deconvolution

Compounded Regularization and Fast Algorithm for Compressive...

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International conference on image and Graphics (ICIG)

作者： Liang Xiao Jun Shao Lili Huang Zhihui Wei School of Computer Science Nanjing University of Science and Technology of PLA Nanjing China

Compressive Sensing Deconvolution (CS Deconvolution) is a new challenge problem encountered in a wide variety of image processing fields. A compound variational regularization model which combined total variation and curve let-based sparsity prior is proposed to recovery blurred image from compressive measurements. We propose a novel fast algorithm using variable-splitting and Dual Douglas-Rachford operator splitting methods. Experiments demonstrate our proposed algorithm can obtain high-resolution data from highly incomplete measurements.

关键词： Deconvolution Extraterrestrial measurements Transforms Compressed sensing image edge detection image reconstruction image coding

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Quantitative and qualitative comparison of SAR images from incomplete measurements using compressed sensing and nonuniform FFT

Quantitative and qualitative comparison of SAR images from i...

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IEEE National conference on Radar

作者： Hamed Kajbaf Joseph T. Case Yahong Rosa Zheng Sergey Kharkovsky Reza Zoughi Electrical and Computer Engineering Department Missouri University of Science and Technology Rolla MO USA

In this paper the performance of two wideband synthetic aperture radar (SAR) imaging methods from incomplete data sets are compared quantitatively and qualitatively. The first approach uses nonuniform fast Fourier transform (NUFFT) SAR to form images from nonuniform spatial and frequency data points. The second approach benefits from the emerging compressed sensing (CS) methodology to recover raw data from undersampled measurements. The results of our experimental tests show that CS has a better performance in terms of error and image contrast while NUFFT SAR has lower computational complexity.

关键词： Three dimensional displays Probes image reconstruction Measurement Fast Fourier transforms Imaging Signal to noise ratio

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Ising field parameter estimation from incomplete and noisy data

Ising field parameter estimation from incomplete and noisy d...

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IEEE International conference on image Processing

作者： J.-F. Giovannelli Groupe Signal-Image Laboratoire de lIntégration du Matériau au Système Université de Bordeaux-CNRS Talence France

The present paper deals with the estimation problem of the Ising field parameter and extends a previous one [1]. It proposes an estimate from indirect observation (incomplete and noisy), whereas the previous paper proposed an estimate from direct observation (complete and noiseless). Both of them are based on an explicit expression for the partition function, known for a long time [2] but, to the best of our knowledge, never used for parameter estimation (except in our previous paper [1]). Both of them are developed in a Bayesian framework. In our previous study (direct observation), the posterior law is explicit but in the present case (indirect observation) the posterior law is not explicit due to the hidden structure. The proposed approach relies on a full Bayesian strategy and a stochastic sampling algorithm (Gibbs sampler including a Metropolis-Hastings step) for posterior exploration. The paper proposes a numerical evaluation of the proposed method.

关键词： Bayesian methods Parameter estimation image reconstruction Markov processes Histograms Joints

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