A VQ-based blind image restoration algorithm

Learning-based algorithms for image restoration and blind image restoration are proposed. Such algorithms deviate from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operator (restoration problem) are utilized for designing the VQ codebooks. The codevectors are designed using the blurred images. For each such vector, the high frequency information obtained from the original images is also available. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the codebooks. For the blind restoration problem, a number of codebooks are designed corresponding to various versions of the blurring function. Given a noisy and blurred image, one of the codebooks is chosen based on a similarity measure, therefore providing the identification of the blur. To make the restoration process computationally efficient, the principal component analysis (PCA) and VQ-nearest neighbor approaches are utilized. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms.     

Bayesian multichannel image restoration using compound Gauss-Markov random fields

We develop a multichannel image restoration algorithm using compound Gauss-Markov random fields (CGMRF) models. The line process in the CGMRF allows the channels to share important information regarding the objects present in the scene. In order to estimate the underlying multichannel image, two new iterative algorithms are presented and their convergence is established. They can be considered as extensions of the classical simulated annealing and iterative conditional methods. Experimental results with color images demonstrate the effectiveness of the proposed approaches.     

Audio-visual interaction in multimodal communication

Multimedia signal processing is more than simply “putting together” text, audio, images, and video. It is the integration and interaction among these different media that creates new systems and new research challenges and opportunities. In multimodal communication where speech is involved, audio-visual interaction is particularly significant     

Motion estimation of skeletonized angiographic images using elastic registration

An approach for estimating the motion of arteries in digital angiographic image sequences is proposed. Binary skeleton images are registered using an elastic registration algorithm in order to estimate the motion of the corresponding arteries. This algorithm operates recursively on the skeleton images by considering an autoregressive (AR) model of the deformation in conjunction with a dynamic programming (DP) algorithm. The AR model is used at the pixel level and provides a suitable cost function to DP through the innovation process. In addition, a moving average (MA) model for the motion of the entire skeleton is used in combination with the local AR model for improved registration results. The performance of this motion estimation method is demonstrated on simulated and real digital angiographic image sequences. It is shown that motion estimation using elastic registration of skeletons is very successful especially with low contrast and noisy angiographic images.     

Comparing interconversion methods between linear viscoelastic material functions

A variety of methods applicable to the interconversion of static (creep) and dynamic (relaxation) functions, with regard to appropriate experimental data of various polymers is investigated and compared. The effectiveness of the selected methods was verified by a series of creep experimental data of various polymeric structures. While most of the employed methods are well established in the literature, some further modifications have been introduced for an improvement of the conversion procedure. Furthermore, a new approach is also employed, which is based on the stretched-exponential function, usually applied to represent both relaxation and retardation functions. It is seen that the examined methods produce a similar result, concerning the creep compliance function, having as a beginning storage and loss modulus experimental data. The same observation applies to the retardation spectra, pointing the fact that discrete spectra deviates significantly from the continuous spectra. As a result, it is shown that the creep compliance function, or the relaxation modulus function, can be predicted using experimental dynamic data (relaxation or creep, respectively), as well as anyone of the examined interconversion methods, with an accuracy close to 5%. The use of approximate or exact relations in the whole procedure was proved not to have a significant effect on the final result (referring mostly to the retardation spectra).     

Parameter estimation in Bayesian reconstruction of SPECT images: An aid in nuclear medicine diagnosis

Despite the adequacy of Bayesian methods to reconstruct nuclear medicine SPECT (single‐photon emission computed tomography) images, they are rarely used in everyday medical practice. This is primarily because of their computational cost and the need to appropriately select the prior model hyperparameters. We propose a simple procedure for the estimation of these hyperparameters and the reconstruction of the original image and test the procedure on both synthetic and real SPECT images. The experimental results demonstrate that the proposed hyperparameter estimation method produces satisfactory reconstructions. Although we have used generalized Gaussian Markov random fields (GGMRF) as prior models, the proposed estimation method can be applied to any priors with convex potential and tractable partition function with respect to the scale hyperparameter. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol 14, 21–27, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.20003     

Super Resolution of Multispectral Images using ℓ1 Image Models and Interband Correlations

In this paper we propose a novel super-resolution based algorithm for the pansharpening of multispectral images. Within the Bayesian formulation, the proposed methodology incorporates prior knowledge on the expected characteristics of multispectral images; that is, it imposes smoothness within each band by means of the energy associated with the ℓ1 norm of vertical and horizontal first order differences of image pixel values and also takes into account the correlation among the bands of the multispectral image. The observation process is modeled using the sensor characteristics of both panchromatic and multispectral images. The method is tested on real and synthetic images, compared with other pansharpening methods, and the quality of the results assessed both qualitatively and quantitatively.     

Preparation and nonlinear optical response of novel palladium-containing micellar nanohybrids

The synthesis and characterization of novel micellar nanohybrids with controllable sizes consisting of palladium nanosized core and covered by well-defined poly(lauryl methacrylate)-block-poly(2-(acetoacetoxy)ethyl methacrylate) (pLauMA-b-pAEMA) diblock copolymers are reported. Their nonlinear optical (NLO) response (i.e. nonlinear refraction and absorption and the third-order susceptibility χ⁽³⁾) is also studied under 35 ps laser excitation both in the visible and in the infrared and found to be insensitive to the size of the micelles while it was found to be greatly affected by the molar fraction of the AEMA block. To the best of our knowledge it is the first time that such Pd micellar nanohybrids are synthesized and investigated towards their NLO properties. The magnitude of the nonlinear optical response of these nanohybrids renders them promising candidates for potential optoelectronic applications.     

On the limit theory of the Gaussian SQMLE in the EGARCH(1,1) model

We derive the limit theory of the Gaussian stable quasi maximum likelihood estimator for the stationary EGARCH(1,1) model when the squared innovation process has marginals with regularly varying tails. We derive regularly varying rates and limiting stable distributions. We perform Monte Carlo experiments to assess the extent of the parameter space corresponding to the invertibility condition, and the quality of the asymptotic approximation.     

Path Representation in Circuit Netlists Using Linear-Sized ZDDs with Optimal Variable Ordering

The efficient representation and manipulation of a large number of paths in a Directed Acyclic Graph (DAG) requires the usage of special data structures that may become of exponential size with respect to the size of the graph. Several methodologies targeting Electronic Design Automation problems such as timing analysis, physical design, verification and testing involve path representation and necessary manipulation. Previous works proposed an encoding using Zero-suppressed Binary Decision Diagrams (ZDDs), which has been shown experimentally to cope well when representing structural or logical paths in VLSI circuits. However, it is well known that the ordering of the variables in a ZDD highly affects its size and, therefore, the efficiency of the methodologies utilizing these data structures. In this work, we show that using a reverse topological order for the ZDD variables bounds the number of nodes in the ZDD representing structural paths to the number of edges in the DAG considered, hence, making the ZDD size linear to the DAG’s size. This result, supported here both theoretically and experimentally, is very important as it can render methodologies with questionable scalability applicable to larger industrial designs. We demonstrate the applicability of the proposed variable ordering in one such methodology which utilizes ZDDs to grade the Path Delay Fault coverage of a given test set.