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The application of the subspace-based optimization method (SOM) in the framework of the method of moments (MoM) is presented so as to reconstruct the relative-permittivity profiles of extended scatterers in transverse electric case. The results of numerical experiments confirm that the SOM is rapidly convergent, robust against noise, capable of reconstructing objects of complex shapes.
Subspace-based optimization methods (SOM) for locating scatterers have been reported recently. The positions and shapes of scatterers are reconstructed by checking the contour of permittivity. Background conditions, including the shape of domain of interest, aperture, and frequency of incident wave, have an influence on the process of permittivity reconstruction. We examine the domain shape effect...
In order to solve the feature reconstruction problem of fMRI time series, hierarchical fast clustering method (HFCM) is proposed. The reconstruction of features can be thought as finding the task-related region of interest (ROI) in the human brain fMRI in order to eliminate information redundary. HFCM takes advantage of optimizing the hierarchical structure and tuning weights of different kind of...
We propose an iterative method for the optimization of a set of 2-D separable transforms for a given training data set. The method outputs orthonormal transforms, each one being optimal for a subset of the data with respect to a sparsity-based objective function. The vertical and horizontal directions of the transform may be different, thus allowing directional-adapted transforms (in contrast to the...
This work focuses on several optimization problems involved in recovery of sparse solutions of linear inverse problems. Such problems appear in many fields including image and signal processing, and have attracted even more interest since the emergence of the compressed sensing (CS) theory. In this paper, we formalize many of these optimization problems within a unified framework of convex optimization...
This paper introduces a new, fast and accurate algorithm for solving problems in the area of compressed sensing, and more generally, in the area of signal and image reconstruction from indirect measurements. This algorithm is inspired by recent progress in the development of novel first-order methods in convex optimization, most notably Nesterov's smoothing technique. In particular, there is a crucial...
We propose a method to demosaick images acquired with a completely arbitrary color filter array (CFA).We adopt a variational approach where the reconstructed image has maximal smoothness under the constraint of consistency with the measurements. This optimization problem boils down to a large, sparse system of linear equations to solve, for which we propose an iterative algorithm. Although the approach...
In this paper, following the Compressed Sensing (CS) paradigm, we study the problem of recovering sparse or compressible signals from uniformly quantized measurements. We present a new class of convex optimization programs, or decoders, coined Basis Pursuit DeQuantizer of moment p (BPDQp), that model the quantization distortion more faithfully than the commonly used Basis Pursuit DeNoise (BPDN) program...
Compressed sensing (CS) is a new area of signal processing for simultaneous signal sampling and compression. Most of existing methods for CS image reconstruction are suitable for piecewise smooth image, but do not behave well on texture-rich natural image. In this paper, a new optimization problem for CS image reconstruction is proposed, in which different regularization terms are introduced for different...
In high dynamic range (HDR) imaging, multiple photographs with different exposure times are combined into a radiance map, which reflects the radiance in real-life scenes. This involves recovering the response function of the imaging process. The technique proposed by Debevec and Malik is a well-known HDR image synthesis algorithm, but the computational complexity is relatively high, which limits the...
Fluidic lens camera systems present a new field of exploration for both the optics and image processing communities. Developed for surgical applications, these cameras do not have moving parts while zooming and they have better miniaturization possibilities. However, the lens causes non-uniform color blur between color planes which creates an image processing problem. We propose the use of a contourlet...
Sparse optimization in overcomplete frames has been widely applied in recent years to ill-conditioned inverse problems. In particular, analysis-based sparse optimization consists of achieving a certain trade-off between fidelity to the observation and sparsity in a given linear representation, typically measured by some ??p quasi-norm. Whereas most popular choice for p is 1 (convex optimization case),...
In this paper, a new joint rate-distortion optimization model based on texture and mean-error factors (TMJRDM) is proposed. Texture factor implies the subjective factor, giving the texture similarity measure between the original image and the target image. At the same time, mean-error factor gives the objective measure. The joint weighted value of texture and mean-error factors is calculated as the...
A high-resolution volumetric 3D display system has been developed, which provides a new viewing modalities for 3D imaging, unlike traditional stereoscopic displays. By ??volumetric 3D display??, we mean that each ??voxel?? in the displayed 3D images locates physically at the (x, y, z) spatial position where it supposes to be, and emits light from that position to form real 3D images in the eyes of...
In this paper a framework for multichannel image restoration based on optimization of the structural similarity (SSIM) index is presented. The SSIM index describes the similarity of images more appropriately for the human visual system than the mean square error (MSE). It has not yet been explored for the multi channel restoration task. The construction of an optimization algorithm is difficult due...
Asymmetric stereoscopic video coding can save the bitrate and maintain the overall visual quality of three dimensional perception. But for mobile three-dimensional television, the original full resolution restoring is complex for the decoder. In this paper, a decoding and up-sampling optimization scheme for asymmetric stereoscopic video coding is proposed, in which macroblocks encoded with SKIP mode...
Perceived from the definition of compressed sensing (CS), the sparser the signal is, the better the recovery will be. Meanwhile, the third-generation wavelet-contourlet is able to sparsely represent signals and detect the singularity of smooth curve. Taking into account the mixed noise from random projection of CS model, we are trying to carry out the following: let the image transformed into contourlet...
In modern driver assistance systems the environment perception plays a decisive role in order to evaluate the current traffic scene. The reliable recognition of the drivable area provides essential information for lane departure warning systems which in turn contribute to active road safety. Most systems on lane recognition do reliable work on well marked roads and under good weather and lighting...
It is now well understood that it is possible to reconstruct sparse signals exactly from what appear to be highly incomplete sets of linear measurements. The form is solution to the optimization problem min ||s||0 , subject to As = x. while this is an NP hard problem, i.e., a non convex problem, therefore researchers try to solve it by constrained l 1-norm minimization and get near-optimal solution...
Regularization technique is a common method for solving inverse problems in image processing. For a complex natural image which contains various structure components, it may be more effective to adopt different regularization terms for different components. An optimization model with component regularization, which was used to solve the problem of compressed sensing image reconstruction, has been...
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