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In this paper, we propose a novel computationally efficient post-processing algorithm to reduce ringing artifacts in the decoded DCT-coded video without using coding information. While the proposed algorithm is based on edge information as most filtering-based de-ringing algorithms do, this algorithm solely uses one single computationally efficient nonlinear filter, namely sigma filter, for both edge detection and smoothing. Specifically, the sigma filter, which was originally designed for nonlinear filtering, is extended to generate edge proximity information. Different from other adaptive filtering-based methods, whose filters typically use a fixed small window but flexible weights, this sigma filter adaptively switches between small and large windows. The adaptation is designed for removing ringing artifacts only, so the algorithm cannot be used for de-blocking. Overall, the proposed algorithm achieves a good balance among removing ringing artifacts, preserving edges and details, and computational complexity.

A finite element method with artificial transmitting boundary incorporated is firstly applied for acousto-optic tunable filters (AOTF) with weighted coupling. Here, two kinds of weighting function are considered, one is Cosine function, the other is Gaussian function. And the numerical results are shown for AOTF with a tapered acoustical directional coupler with different weighing function on a LiNbO 3 substrate. Through comparison, the optical filter responses of the TE-TM mode conversions of AOTF with Gaussian weighting function are better than AOTF with Cosine weighting function. The results are in good agree with others, which illustrates this method is useful in calculation on AOTF.

A hybrid weighted/interacting particle filter, the selectively resampling particle (SERP) filter, is used to detect and track an unknown number of independent targets on a one-dimensional "racetrack" domain. The targets evolve in a nonlinear manner. The observations model a sensor positioned above the racetrack. The observation data takes the form of a discretized image of the racetrack, in which each discrete segment has a value depending both upon the presence or absence of targets in the corresponding portion of the domain, and upon lognormal noise. The SERP filter provides a conditional distribution approximated by particle simulations. After each observation is processed, the SERP filter selectively resamples its particles in a pairwise fashion, based on their relative likelihood. We consider a reinforcement learning approach to control this resampling. We compare two different ways of applying the filter to the problem: the signal measure approach and the model selection approach. We present quantitative results of the ability of the filter to detect and track the targets, for each of the techniques. Comparisons are made between the signal measure and model selection approaches, and between the dynamic and static resampling control techniques.© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Three-dimensional (3-D) subband/wavelet coding using a motion compensated temporal filter (MCTF) is emerging as a very effective structure for highly scalable video coding. Most previous work has used two-tap Haar filters for the temporal analysis/synthesis. To make better use of the temporal redundancies, we are proposing an MCTF scheme based on longer biorthogonal filters. We show a lifting based coder capable of subpixel accurate motion compensation. If we retain the fixed size GOP structure of the Haar filter MCTFs, we need to use symmetric extensions at both ends of the GOP. This gives rise to loss of coding efficiency at the GOP boundaries resulting in significant PSNR drops there. This performance can be considerably improved by using a 'sliding window,' in place of the GOP block. We employ the 5/3 filter and its non-orthogonality causes PSNR variation, which can be reduced by employing filter-based weighting coefficients. Overall the longer filters have a higher coding gain than the Haar filters and show significant improvement in average PSNR at high bit rates. However, a doubling in the number of motion vectors to be transmitted, translates to a drop in PSNR at the lower video bit rates.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The conventional filters can't achieve good effects in reducing speckle for high resolution single-look spaceborne synthetic aperture radar(SAR) images. In this paper an algorithm on efficiently reducing speckle is developed. This algorithm uses multiple structuring elements to replace common structuring elements so as to create an omni-directional multiple structuring elements soft-morphological filter whose weight values can be obtained through the improved impulse Bp neural network(NN) self-adaptive method. The performance of this algorithm is analyzed in detail. Finally, the raw data of RADARSAT is used to demonstrate its efficiency. The result shows that the filter can bring better effect than other filters.

A new unsupervised multiresolution pyramidal edge detector, based on adaptive weighted fuzzy mean (AWFM) filters, is presented in this paper. The algorithm first constructs a pyramidal structure by repetitive AWFM filtering and subsampling of original image. Then it utilizes multiple heuristic linking criteria between the edge nodes of two adjacent levels and considers the linkage as a fuzzy model, which is trained offline. Through this fuzzy linking model, the boundaries detected at coarse resolution are propagated and refined to the bottom level from the coarse-to-fine edge detection. The validation experiments results demonstrate that the proposed approach has superior performance compared with standard fixed resolution detector and previous multiresolution approach, especially in impulse noise environment.