Tomographic image reconstruction from incomplete view data by convex projections and direct fourier inversion

We consider the problem of reconstructing CAT imagery by the direct Fourier method (DFM) when not all view data are available. To restore the missing information we use the method of projections onto convex sets (POCS). POCS is a recursive image restoration technique that finds a solution consistent with the measured data and a priori known constraints in both the space and Fourier domain. Because DFM reconstruction is a frequency-domain technique it is ideally matched to POCS restoration when, for one reason or another, we are forced to generate an image from a less than complete set of view data. We design and apply an algorithm (PRDF) which interpolates/extrapolates the missing Fourier domain information by POCS and reconstructs an image by DFM. A simulated human thorax cross section is restored and reconstructed. The restorations using POCS are compared with the Gerchberg-Papoulis extrapolation method and shown to be superior. Applications of PRDF to other types of medical imaging modalities are discussed.     

Superresolution video reconstruction with arbitrary samplinglattices and nonzero aperture time

Printing from an NTSC source and conversion of NTSC source material to high-definition television (HDTV) format are some of the applications that motivate superresolution (SR) image and video reconstruction from low-resolution (LR) and possibly blurred sources. Existing methods for SR image reconstruction are limited by the assumptions that the input LR images are sampled progressively, and that the aperture time of the camera is zero, thus ignoring the motion blur occurring during the aperture time. Because of the observed adverse effects of these assumptions for many common video sources, this paper proposes (i) a complete model of video acquisition with an arbitrary input sampling lattice and a nonzero aperture time, and (ii) an algorithm based on this model using the theory of projections onto convex sets to reconstruct SR still images or video from an LR time sequence of images. Experimental results with real video are provided, which clearly demonstrate that a significant increase in the image resolution can be achieved by taking the motion blurring into account especially when there exists large interframe motion.     

Efficient multiframe Wiener restoration of blurred and noisy imagesequences

Computationally efficient multiframe Wiener filtering algorithms that account for both intraframe (spatial) and interframe (temporal) correlations are proposed for restoring image sequences that are degraded by both blur and noise. One is a general computationally efficient multiframe filter, the cross-correlated multiframe (CCMF) Wiener filter, which directly utilizes the power and cross power spectra of only NxN matrices, where N is the number of frames used in the restoration. In certain special cases the CCMF lends itself to a closed-form solution that does not involve any matrix inversion. A special case is the motion-compensated multiframe (MCMF) filter, where each frame is assumed to be a globally shifted version of the previous frame. In this case, the interframe correlations can be implicitly accounted for using the estimated motion information. Thus the MCMF filter requires neither explicit estimation of cross correlations among the frames nor matrix inversion. Performance and robustness results are given.     

Simultaneous motion estimation and segmentation

We present a Bayesian framework that combines motion (optical flow) estimation and segmentation based on a representation of the motion field as the sum of a parametric field and a residual field. The parameters describing the parametric component are found by a least squares procedure given the best estimates of the motion and segmentation fields. The motion field is updated by estimating the minimum-norm residual field given the best estimate of the parametric field, under the constraint that motion field be smooth within each segment. The segmentation field is updated to yield the minimum-norm residual field given the best estimate of the motion field, using Gibbsian priors. The solution to successive optimization problems are obtained using the highest confidence first (HCF) or iterated conditional mode, (ICM) optimization methods. Experimental results on real video are shown.     

Robust, object-based high-resolution image reconstruction fromlow-resolution video

We propose a robust, object-based approach to high-resolution image reconstruction from video using the projections onto convex sets (POCS) framework. The proposed method employs a validity map and/or a segmentation map. The validity map disables projections based on observations with inaccurate motion information for robust reconstruction in the presence of motion estimation errors; while the segmentation map enables object-based processing where more accurate motion models can be utilized to improve the quality of the reconstructed image. Procedures for the computation of the validity map and segmentation map are presented. Experimental results demonstrate the improvement in image quality that can be achieved by the proposed methods.     

Estimation of the parameters of a multipath channel usingset-theoretic deconvolution

A method for the estimation of the parameters of multipath communication channels is developed. The method makes use of matched filtering, set-theoretic deconvolution and autoregressive modeling. Pulse compression waveforms are used as channel probing signals. The results of a feasibility study in which the proposed method is applied to specular multipath channels to estimate the multipath parameters at a number of different signal-to-noise ratios (SNR) are presented     

高动态范围LCD显示中区域控制型LED背光驱动信号的导出

LED背光技术不仅显著增强了LCD显示的动态范围,同时使现有图像达到了更逼真的显示效果。通过对LED单独控制,可为显示提供区域可控的LED背光。然而,LED之间的干扰在这里不仅仅为我们提供了较高的亮度,同时也是产生潜在虚假图像的原因,这使得导出最佳LED驱动信号成为一项具有挑战性的工作。本文通过两个数学模型:迭代解旋法和线性优化法,计算分析了上述问题。并给出了这两个数学模型的算法,同时建立了一个包含161个高动态范围图像的图像数据库,对上述两种方法的效果进行了评估。     

Quantitative analysis of artifacts in linear space-invariant image restoration

Several image restoration algorithms exist in the literature ranging from deterministic iterative techniques to optimum recursive methods. Unfortunately, all these algorithms produce undesirable artifacts in the process of undoing the degradations because of the ill-posed nature of the image restoration problem. This paper provides a complete quantitative analysis of different artifacts caused by linear shift-invariant (LSI) image restoration methods. The aim of this paper is to mathematically show how these artifacts originate in the general case of an arbitrary blur point spread function and an arbitrary LSI restoration filter, and then to study the characteristics of these artifacts in the special cases of uniform motion blur and out-of-focus blur via experimental analysis. Several pictures that illustrate these artifacts are presented. We discuss strategies for the suppression of these artifacts based on the analysis provided.This paper is based upon research performed under NSF grants MIP-8809291 and CDA-8820693, and Grant No. 88-IJ-CX-0038 from the National Institute of Justice to the University of Rochester.     

Effect of plastic media blasting method on mechanical properties of Al 2024-T6 alloy

We investigated the effect of Plastic media blasting (PMB) method on mechanical properties of Al 2024-T6 alloy for aircraft body coatings. Painting-stripping process with three cycles was performed using PMB for three different thicknesses. The relationship between surface morphology-deformation and defects was characterized. PMB affects the fatigue life and surface morphology of specimens. The fatigue life decreases when the specimen thickness decreases. In contrast, this method does not significantly affect the fatigue life of thick specimens. The surface roughness was between 0.30-0.65 Ra for processed specimens. The study will determine proper sheet metal thickness which is not affected seriously because of the method for Al 2024-T6 alloy.