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Sparse modeling is one of the efficient techniques for imaging that allows recovering lost information. In this paper, we present a novel iterative phase-retrieval algorithm using a sparse representation of the object amplitude and phase. The algorithm is derived in terms of a constrained maximum likelihood, where the wave field reconstruction is performed using a number of noisy intensity-only observations with a zero-mean additive Gaussian noise. The developed algorithm enables the optimal solution for the object wave field reconstruction. Our goal is an improvement of the reconstruction quality with respect to the conventional algorithms. Sparse regularization results in advanced reconstruction accuracy, and numerical simulations demonstrate significant enhancement of imaging., Comment: Submitted to the 10th IMEKO Symposium LMPMI (Laser Metrology for Precision Measurement and Inspection in Industry) on May 31, 2011

A novel fast recursive coding technique is proposed. It operates with only integer values not longer 8 bits and is multiplication free. Recursion the algorithm is based on indirectly provides rather effective coding of symbols for very large alphabets. The code length for the proposed technique can be up to 20-30% less than for arithmetic coding and, in the worst case it is only by 1-3% larger., Comment: 3 pages, submitted to IEEE Transactions on Information Theory

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In this paper, a novel algorithm for inference in Markov Random Fields (MRFs) is presented. Its goal is to find approximate maximum a posteriori estimates in a simple manner by combining neighbourhood influence of iterated conditional modes (ICM) and message passing of loopy belief propagation (LBP). We call the proposed method neighbourhood-consensus message passing because a single joint message is sent from the specified neighbourhood to the central node. The message, as a function of beliefs, represents the agreement of all nodes within the neighbourhood regarding the labels of the central node. This way we are able to overcome the disadvantages of reference algorithms, ICM and LBP. On one hand, more information is propagated in comparison with ICM, while on the other hand, the huge amount of pairwise interactions is avoided in comparison with LBP by working with neighbourhoods. The idea is related to the previously developed iterated conditional expectations algorithm. Here we revisit it and redefine it in a message passing framework in a more general form. The results on three different benchmarks demonstrate that the proposed technique can perform well both for binary and multi-label MRFs without any limitations on the model definition. Furthermore, it manifests improved performance over related techniques either in terms of quality and/or speed.

'View plus depth' is a 3D video representation where a single color video channel is augmented with per-pixel depth information in the form of gray-scale video sequence. This representation is a good candidate for 3D video delivery applications, as it is display agnostic and allows for some parallax adjustments. However, the quality of the associated depth is an issue, as the depth channel is usually a result of estimation procedure based on stereo correspondences or comes from a noisy and low-resolution range sensor. Therefore, proper filtering of the depth channel is needed before it is used for compression and/or view rendering. The problem is even more pronounced in video, where temporal consistency of the depth sequence is required. In this paper, we propose a filtering approach to refine the quality of noisy, blocky, and temporally-inconsistent depth maps. We utilize color constraints from the video channel and modify a previous super-resolution approach to tackle the time consistency for video. Our implementation is fast and highly memory efficient. We present filtering results demonstrating the superiority of the developed technique.

Under the framework of the FP-7 European MobiThin project, the present study addresses the issue of remote display representation for mobile thin client. The main issue is to design a compressing algorithm for heterogeneous content (text, graphics, image and video) with low-complex decoding. As a first step in this direction, we propose a novel software architecture, based on BiFS - Binary Format for Scenes (MPEG-4 Part 11). On the server side, the graphical content is parsed, converted and binary encoded into the BiFS format. This content is then streamed to the terminal, where it is played on a simple MPEG player. The viability of this solution is validated by comparing it to the most intensively used wired solutions, e.g. VNC - Virtual Network Computing.