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In this paper, we look at the problem of spatially scaling color images. We focus on an approach that takes advantage of the human visual system's color spatial frequency sensitivity. The algorithm performs an efficient least-squares (LS) resolution conversion for the luminance channel and a low-complexity pixel replication/reduction in the chrominance channels. The performance of the algorithm is compared to a LS method in sRGB and CIELAB color spaces, as well as standard bilinear interpolation in sRGB space. The comparisons are made in terms of computational cost and color error in sCIELAB. [ABSTRACT FROM AUTHOR]

Measurement of image or video quality is crucial for many image-processing algorithms, such as acquisition, compression, restoration, enhancement, and reproduction. Traditionally, image quality assessment (QA) algorithms interpret image quality as similarity with a "reference" or "perfect" image. The obvious limitation of this approach is that the reference image or video may not be available to the QA algorithm. The field of blind, or no-reference, QA, in which image quality is predicted without the reference image or video, has been largely unexplored, with algorithms focussing mostly on measuring the blocking artifacts. Emerging image and video compression technologies can avoid the dreaded blocking artifact by using various mechanisms, but they introduce other types of distortions, specifically blurring and ringing. In this paper, we propose to use natural scene statistics (NSS) to blindly measure the quality of images compressed by JPEG2000 (or any other wavelet based) image coder. We claim that natural scenes contain nonlinear dependencies that are disturbed by the compression process, and that this disturbance can be quantified and related to human perceptions of quality. We train and test our algorithm with data from human subjects, and show that reasonably comprehensive NSS models can help us in making blind, but accurate, predictions of quality. Our algorithm performs close to the limit imposed on useful prediction by the variability between human subjects. [ABSTRACT FROM AUTHOR]

Without additional resources, it is often impossible to transform one entangled quantum state into another with local quantum operations and classical communication. Jonathan and Plenio (Phys. Rev. Left., vol. 83, p. 3566, 1999) presented an interesting example showing that the presence of another state, called a catalyst, enables such a transformation without changing the catalyst. They also pointed out that in general it is very hard to find an analytical condition under which a catalyst exists. In this paper, we study the existence of catalysts for two incomparable quantum states. For the simplest case of 2 × 2 catalysts for transformations from one 4 × 4 state to another, a necessary and sufficient condition for existence is found. For the general case, we give an efficient polynomial time algorithm to decide whether a k × k catalyst exists for two n × n incomparable states, where k is treated as a constant. [ABSTRACT FROM AUTHOR]

We present a two-stage method for obtaining both phase and object estimates from phase-diversity time series data. In the first stage, the phases are estimated for each time frame using the limited memory BEGS method. In the second stage, an algorithm that incorporates a nonnegativity constraint as well as prior knowledge of data noise statistics is used to obtain an estimate of the object being observed. The approach is tested on real phase-diversity data with 32 time frames, and a comparison is made between it and a previously developed approach. Also, the image deblurring algorithm in stage two is tested against other standard methods and is shown to be the best for our problem. [ABSTRACT FROM AUTHOR]

An efficient algorithm is presented for the computation of grayscale morphological operations with arbitrary 2-D flat structuring elements (S.E.). The required computing time is independent of the image content and of the number of gray levels used. It always outperforms the only existing comparable method, which was proposed in the work by Van Droogenbroeck and Talbot, by a factor between 3.5 and 35.1, depending on the image type and shape of S.E. So far, filtering using multiple S.E.s is always done by performing the operator for each size and shape of the S.E. separately. With our method, filtering with multiple S.E.s can be performed by a single operator for a slightly reduced computational cost per size or shape, which makes this method more suitable for use in granulometries, dilation-erosion scale spaces, and template matching using the hit-or-miss transform. The discussion focuses on erosions and dilations, from which other transformations can be derived. [ABSTRACT FROM AUTHOR]

The article reports on the significant role of data association and model selection for tracking multiple targets in a dense clutter environment without using a priopri information about the target dynamic. The authors propose a neutral-network based tracking algorithm, incorporating an interacting multiple model. The proposed method overcomes the problem of data association by using the method of expectation maximization (EM).

This work addresses the derivation of the phase difference-based maximum likelihood (ML) phase unwrapping algorithm. To this end, we derive the joint statistics of the phase differences on a two-dimensional grid for the multichannel case, where several scaled wrapped phase values are available. Subsequently, we determine and study the structure of the phase difference-based ML estimator and compare it to known phase unwrapping techniques. This work allows us to frame single and multichannel algorithms in a common formulation. Moreover, among the known single-channel phase difference-based procedures, we identify those attaining an ML solution. We also show that multichannel phase difference-based and, recently proposed, phase-based ML algorithms achieve equivalent solutions. [ABSTRACT FROM AUTHOR]

Viewed abstractly, all the algorithms considered here are designed to provide a nonnegative solution x to the system of linear equations y = Px, where y is a vector with positive entries and P a matrix whose entries are nonnegative and with no purely zero columns. The expectation maximization maximum likelihood method, as it occurs in emission tomography, and the simultaneous multiplicative algebraic reconstruction technique are slow to converge on large data sets; accelerating convergence through the use of block-iterative or ordered subset versions of these algorithms is a topic of considerable interest. These block-iterative versions involve relaxation and normalization parameters, the correct selection of which may not be obvious to all users. The algorithms are not faster merely by virtue of being block-iterative; the correct choice of the parameters is crucial. Through a detailed discussion of the theoretical foundations of these methods, we come to a better understanding of the precise roles these parameters play. [ABSTRACT FROM AUTHOR]

We study the capacity of multiple-input multiple- output (MIMO) relay channels. We first consider the Gaussian MIMO relay channel with fixed channel conditions, and derive upper bounds and lower bounds that can be obtained numerically by convex programming. We present algorithms to compute the bounds. Next, we generalize the study to the Rayleigh fading case. We find an upper bound and a lower bound on the ergodic capacity. It is somewhat surprising that the upper bound can meet the lower bound under certain regularity conditions (not necessarily degradedness), and therefore the capacity can be characterized exactly; previously this has been proven only for the degraded Gaussian relay channel. We investigate sufficient conditions for achieving the ergodic capacity; and in particular, for the case where all nodes have the same number of antennas, the capacity can be achieved under certain signal-to-noise ratio (SNR) conditions. Numerical results are also provided to illustrate the bounds on the ergodic capacity of the MIMO relay channel over Rayleigh fading. Finally, we present a potential application of the MIMO relay channel for cooperative communications in ad hoc networks. [ABSTRACT FROM AUTHOR]