High-resolution images from low-resolution compressed video

Surveys the field of super resolution (SR) processing for compressed video. The introduction of motion vectors, compression noise, and additional redundancies within the image sequence makes this problem fertile ground for novel processing methods. In conducting this survey, though, we develop and present all techniques within the Bayesian framework. This adds consistency to the presentation and facilitates comparison between the different methods. The article is organized as follows. We define the acquisition system utilized by the surveyed procedures. Then we formulate the HR problem within the Bayesian framework and survey models for the acquisition and compression systems. This requires consideration of both the motion vectors and transform coefficients within the compressed bit stream. We survey models for the original HR image intensities and displacement values. We discuss solutions for the SR problem and provide examples of several approaches.     

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     

Time Dependence and Moments of a Family of Time-Varying Parameter Garch in Mean Models

Abstract.  In this paper we consider the time series dependence, stationarity, and higher moments issues of a family of first-order conditionally heteroskedastic in mean models with a possibly time-varying mean parameter. The interest in these models lies in the fact that economic theory and physics often require the connection between the first and second conditional moments of time series. Our results reveal important properties of these models, which are consistent with stylized facts in financial and turbulence data sets. They can also be employed for model identification, estimation, and testing.     

Random neural networks with synchronized interactions

Large-scale distributed systems, such as natural neuronal and artificial systems, have many local interconnections, but they often also have the ability to propagate information very fast over relatively large distances. Mechanisms that enable such behavior include very long physical signaling paths and possibly saccades of synchronous behavior that may propagate across a network. This letter studies the modeling of such behaviors in neuronal networks and develops a related learning algorithm. This is done in the context of the random neural network (RNN), a probabilistic model with a well-developed mathematical theory, which was inspired by the apparently stochastic spiking behavior of certain natural neuronal systems. Thus, we develop an extension of the RNN to the case when synchronous interactions can occur, leading to synchronous firing by large ensembles of cells. We also present an O(N3) gradient descent learning algorithm for an N-cell recurrent network having both conventional excitatory-inhibitory interactions and synchronous interactions. Finally, the model and its learning algorithm are applied to a resource allocation problem that is NP-hard and requires fast approximate decisions.     

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     

Assessing low-carbon energy technologies against sustainability and resilience criteria: results of a European experts survey

The aim of this article is the assessment of low-carbon energy technologies in Europe against a set of sustainability and resilience criteria. The assessment was based on a survey that was conducted among 40 European experts. Solar Photovoltaic was the technology that proved to achieve high performance against many criteria, whereas nuclear was assessed by the experts with relatively low performance against most of the criteria. Furthermore, it became evident that there is high degree of convergence between the experts, and therefore high degree of confidence, on specific aspects such as the high level of public resistance against Nuclear and the low stability of energy generation of wind onshore. The experts had major disagreements on the performance of carbon capture and storage (CCS) technologies against technological maturity and innovative ability criteria, which reveals a high degree of uncertainty on how CCS will be deployed in the future, pointing out a direction for future research.