In general, Micropower Impulse Radar (MIR) depends on Ultra-Wideband (UWB) transmission systems. UWB technology can supply innovative new systems and products that have an obvious value for radar and communications uses. Important applications include bridge-deck inspection systems, ground penetrating radar, mine detection, and precise distance resolution for such things as liquid level measurement. Most of these UWB inspection and measurement methods have some unique qualities, which need to be pursued. Therefore, in considering changes to Part 15 the FCC needs to take into account the unique features of UWB technology. MIR is applicable to two general types of UWB systems: radar systems and communications systems. Currently LLNL and its licensees are focusing on radar or radar type systems. LLNL is evaluating MIR for specialized communication systems. MIR is a relatively low power technology. Therefore, MIR systems seem to have a low potential for causing harmful interference to other users of the spectrum since the transmitted signal is spread over a wide bandwidth, which results in a relatively low spectral power density.

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Response to FCC 98-208 notice of inquiry in the matter of revision of part 15 of the commission's rules regarding ultra-wideband transmission systems

Ultra Wideband Signals and Systems in Communication Engineering

We present the TESTIMAGES archive, a huge and free collection of sample images designed for analysis and quality assessment of different kinds of displays (i.e. monitors, televisions and digital cinema projectors) and image processing tecnhiques. The archive includes more than 2 million images originally acquired and divided in four different categories: SAMPLING and SAMPLING_PATTERNS (aimed at testing resampling algorithms), COLOR (aimed at testing color rendering on different displays) and PATTERNS (aimed at testing the rendering of standard geometrical patterns). The archive is currently online as a SourceForge project and, even if not yet publicized in the scientific community, it has already been used in different contexts and cited in scientific publications. We plan to extend the archive including datasets for other kinds of specific analyses.

TESTIMAGES: a Large-scale Archive for Testing Visual Devices and Basic Image Processing Algorithms

Video enhancement is one of the most important and difficult components in video research. The aim of video enhancement is to improve the visual appearance of the video, or to provide a "better"transform representation for future automated video processing, such as analysis, detection, segmentation, recognition, surveillance, traffic, criminal justice systems. In this paper, we present an overview of video enhancement processing and analysis algorithms used in these applications. The existing techniques of video enhancement can be classified into two categories: Self-enhancement and Context- based fusion enhancement. More specifically, we categorize processing methods based representative techniques of video enhancement. Thus, the contribution of the paper has fourfold: (1) to classify and review video enhancement processing algorithms, (2) to discuss the advantages and disadvantages of these algorithms, (3) according to this integrated consideration, attempt an evaluation of shortcomings and general needs in this field of active research, and (4) we will point out promising directions on research for video enhancement for future research.

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A Survey of Video Enhancement Techniques

A robust multiframe super-resolution algorithm based on half-quadratic estimation with modified BTV regularization

The traditional SRR (Super-Resolution Reconstruction) estimations are based on L1 or L2 statistical norm estimation therefore these SRR methods are usually very sensitive to their assumed model of data and noise that limits their utility. This paper reviews some of these SRR methods and addresses their shortcomings. We propose a novel SRR approach based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. The Huber norm [10] is used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image, removing outliers in the data and Tikhonov and Huber-Tikhonov regularization are used to remove artifacts from the final answer and improve the rate of convergence. The experimental results confirm the effectiveness of our methods and demonstrate its superiority to other super-resolution methods based on L1 and L2 norm for a several noise models such as Noiseless, AWGN, Poisson and Salt & Pepper Noise.

A Robust Iterative Multiframe Super-Resolution Reconstruction using a Huber Bayesian Approach with Huber-Tikhonov Regularization

Traditionally, the problems of applying Orthogonal Matching Pursuit (OMP) to large images are its high computing time and its requirement for a large matrix. In this paper, we propose a fast image recovery algorithm by dividing the image into block of n×n pixels and applying OMP to each n×n block instead of the entire image. The key idea is that small matrix requires less computing time and less memory. In the experiment, the block based OMP was applied to three standard test images: Lena, Mandrill and Pirate. Compared to standard OMP, block based OMP required less computing time while giving comparable PSNR.

A fast image recovery using compressive sensing technique with block based Orthogonal Matching Pursuit

In the research operation of Digital Signal Processing (DSP) and Digital Image Processing (DIP), one of the most essential obstacles is the image denoise algorithm by the reason of a very large demand of high quality noise-free images therefore there are many image denoise algorithms have been invented in the time of two decades. Bilateral filter is one of the most impressive and feasible algorithms, which is usually applied for denoise propose, but the performance of the Bilateral filter is substantially bank on three parameters: spatial variance, radiometric variance and window size. Consequently, this paper investigates the performance influence impact of spatial variance, radiometric variance, window size for the Bilateral Filter in the denoise propose. In the denoise experiment, Bilateral filter (BF) is applied on three noisy standard images under five Gaussian noise power levels and the best results in the PSNR prospective point of view from deniose algorithm is picked. Moreover, an optimal value of three parameters: spatial variance, radiometric variance, window size, which make the performance of Bilateral filter the highest PSNR, are extensively investigated for each types of tested images and each noise powers.

The bilateral denoising performance influence of window, spatial and radiometric variance

This paper presents a performance analysis of 3 popular optical flow algorithms (2D optical flow block-based full search algorithm (BOF), Horn-Schunk algorithm (HS) and Lucas-Kanade algorithm (LK)) under the noise conditions. And the confidence based optical flow algorithm for high reliability (CBOF) is applied on these 3 algorithms over different characteristic of standard sequences with several dB of Additive White Gaussian Noise (AWGN). For algorithm of HS and LK, we also applied the kernel model of Barron, Fleet, and Beauchemin (BFB) on these algorithms in our experiment. Especially in HS algorithm, we also investigate the performance on the best average smoothness weight (α) which is prior evaluated by Darun K. and Vorapoj P‥ These experiment results are comprehensively tested on several standard sequences such as AKIYO, COASTGUARD, CONTAINER, and FOREMAN that have different foreground and background movement characteristic in a level of 0.5 sub-pixel displacement. Each standard sequence has 4 sets of sequence included an original (no noise), AWGN 25 dB (low noise), AWGN 20 dB, and AWGN 15 dB (high noise) respectively which concentrated on Peak Signal to Noise Ratio (PSNR) as the performance indicator in our experiment.

Experimental performance analysis of High Confidence Reliability based on differential optical flow algorithms over AWGN sequences with sub-pixel displacement

As a rule, the performance of almost digital image processing (DIP) algorithms and these applications directly depends on the spatial resolution of observed input images. Unfortunately, from the current image sensor technology, it is hard to take sufficient high spatial resolution images from commercial devices therefore the fantastic research attempts and, consequently, simple digital image resolution enhancements have been boosted in the last decade. The high-frequency image prediction reconstruction is the simple and effective algorithm for enhancing the image resolution however this algorithm is strongly depends on the edge detection kernel and M 0 parameter. Therefore, this paper studies a performance impact of an edge detection kernel such as Roberts kernel, Prewitt Kernel, Sobel Kernel, Laplacian Kernel and Laplacian of Gaussian (LOG) Kernel for the high-frequency image prediction reconstruction. This paper presents three experimental performance studies under a noiseless environment, several blurred environments at different blurred variance and several noisy environments at different noise power levels. The first performance study is an empirical exhaustive study of an optimal edge detection kernel and the study of optimal M 0 value is experimentally determined under this environment. The second performance study is an empirical exhaustive study of an optimal edge detection kernel and the study of optimal M 0 value is experimentally determined under these environments. Finally, the last performance study is an empirical exhaustive study of an optimal edge detection kernel and the study of optimal M 0 value is experimentally determined under these environments.

Vorapoj Patanavijit

Papers

A Robust Iterative Multiframe Super-Resolution Reconstruction using a Huber Bayesian Approach with Huber-Tikhonov Regularization

A fast image recovery using compressive sensing technique with block based Orthogonal Matching Pursuit

The bilateral denoising performance influence of window, spatial and radiometric variance

Experimental performance analysis of High Confidence Reliability based on differential optical flow algorithms over AWGN sequences with sub-pixel displacement

A performance impact of an edge kernel for the high-frequency image prediction reconstruction