Introduction to Thematic Mapper investigations. Section 1: Radiometry. Section 2: Geometry

An overview of papers which deal with radiometric characterization of the TM sensor is presented. Spectral characteristics are summarized. The geometric accuracy of TM are also examined. Aspects of prelaunch and post launch sensor performance, ground processing techniques, and error correction are also investigated

Image enlargement using fractal

Centre for Multimedia Signal Processing, Department of Electronic and Information EngineeringRefereed conference paper2002-2003 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Fast Interpolation Algorithms for Real-Time Three-Dimensional Cardiac Ultrasound

Real-time three-dimensional (RT3D) ultrasound technique based on matrix phased array transducers is likely to become predominant for dynamic screening in cardiac and obstetric practice. With these transducers large data volumes are acquired in spherical coordinates and require resampling to be visualized in cartesian coordinates. A fast 3D resampling method was implemented and five interpolation kernels tested on cardiac RT3D data. Downsizing and smoothing of sampling artifacts were integrated in the resampling process for improvement of the reconstructed data visual quality

Quantitative Comparison of Sinc-Approximating Kernels for Medical Image Interpolation

Abstract. Interpolation is required in many medical image processing operations. From sampling theory, it follows that the ideal interpolation kernel is the sinc function, which is of infinite extent. In the attempt to obtain practical and computationally efficient image processing al-gorithms, many sinc-approximating interpolation kernels have been de-vised. In this paper we present the results of a quantitative comparison of 84 different sinc-approximating kernels, with spatial extents ranging from 2 to 10 grid points in each dimension. The evaluation involves the application of geometrical transformations to medical images from dif-ferent modalities (CT, MR, and PET), using the different kernels. The results show very clearly that, of all kernels with a spatial extent of 2 grid points, the linear interpolation kernel performs best. Of all kernels with an extent of 4 grid points, the cubic convolution kernel is the best (28 %- 75 % reduction of the errors as compared to linear interpolation). Even better results (44 %- 95 % reduction) are obtained with kernels of larger extent, notably the Welch, Cosine, Lanczos, and Kaiser windowed sinc kernels. In general, the truncated sinc kernel is one of the worst performing kernels.

Frequency Analysis of Gradient Estimators in Volume Rendering

Gradient information is used in volume rendering to classify and color samples along a ray. In this paper, we present an analysis of the theoretically ideal gradient estimator and compare it to some commonly used gradient estimators. A new method is presented to calculate the gradient at arbitrary sample positions, using the derivative of the interpolation filter as the basis for the new gradient filter. As an example, we will discuss the use of the derivative of the cubic spline. Comparisons with several other methods are demonstrated. Computational efficiency can be realized since parts of the interpolation computation can be leveraged in the gradient estimatio

Focus determination for the James Webb Space Telescope Science Instruments: A Survey of Methods

The James Webb Space Telescope (JWST) is a segmented deployable telescope that will require on-orbit alignment using the Near Infrared Camera as a wavefront sensor. The telescope will be aligned by adjusting seven degrees of freedom on each of 18 primary mirror segments and five degrees of freedom on the secondary mirror to optimize the performance of the telescope and camera at a wavelength of 2 microns. With the completion of these adjustments, the telescope focus is set and the optical performance of each of the other science instruments should then be optimal without making further telescope focus adjustments for each individual instrument. This alignment approach requires confocality of the instruments after integration and alignment to the composite metering structure, which will be verified during instrument level testing at Goddard Space Flight Center with a telescope optical simulator. In this paper, we present the results from a study of several analytical approaches to determine the focus for each instrument. The goal of the study is to compare the accuracies obtained for each method, and to select the most feasible for use during optical testing

OPTIMIZATION OF THE 3P KEYS KERNEL PARAMETERS BY MINIMIZING THE RIPPLE OF THE SPECTRAL CHARACTERISTIC

The ideal interpolation kernel is described by the sinc function, and its spectral characteristic is the box function. Due to the infinite length of the ideal kernel, it is not achievable. Therefore, convolutional interpolation kernels of finite length, which should better approximate the ideal kernel in a specified interval, are formed. The approximation function should have a small numerical complexity, so as to reduce the interpolation execution time. In the scientific literature, great attention is paid to the polynomial kernel of the third order. However, the time and spectral characteristic of the third-order polynomial kernels differs significantly from the shape of the ideal kernel. Therefore, the accuracy of cubic interpolation is lower. By optimizing the kernel parameters, it is possible to better approximate the ideal kernel. This will increase the accuracy of the interpolation. The first part of the paper describes a three-parameter (3P) Keys interpolation kernel, r. After that, the algorithm for optimizing the parameters of the 3P Keys kernel, is shown. First, the kernel is disassembled into components, and then, over each kernel component, Fourier transform is applied. In this way the spectral characteristic of the 3P Keys kernel, H, was determined. Then the spectral characteristic was developed in the Taylor series, HT. With the condition for the elimination of the members of the Taylor series, which greatly affect the ripple of the spectral characteristic, the optimal kernel parameters (αopt, βopt, gopt) were determined. The second part of the paper describes an experiment, in which the interpolation accuracy of the 3P Keys kernel, was tested. Parametric cubic convolution (PCC) interpolation, with the 3P kernel, was performed over the images from the Test database. The Test database is created with standard Test images, which are intensively used in Digital Image Processing. By analyzing the interpolation error, which is represented by the Mean Square Error, MSE, the accuracy of the interpolation was determined. The results (αopt, βopt, gopt, MSEmin) are presented on tables and graphs. Detailed comparative analysis showed higher interpolation accuracy with the proposed 3P Keys interpolation kernel, compared to the interpolation accuracy with, 1P Keys and 2P Keys interpolation kernels. Finally, the numerical values of the optimal kernel parameters, which are determined by the optimization algorithm proposed in this paper, were experimentally verified

Three-Parametric Cubic Interpolation for Estimating the Fundamental Frequency of the Speech Signal

In this paper, we propose a three-parametric convolution kernel which is based on the one-parameter Keys kernel. The first part of the paper describes the structure of the three-parameter convolution kernel. Then, a certain analytical expression for finding the position of the maximum of the reconstructed function is given. The second part presents an algorithm for estimating the fundamental frequency of the speech signal processing in the frequency domain using Picking Picks methods and parametric cubic convolution. Furthermore, the results of experiments give the estimated fundamental frequency of speech and sinusoidal signals in order to select the optimal values of the parameters of the proposed convolution kernel. The results of the fundamental frequency estimation according to the mean square error are given by tables and graphics. Consequently, it is used as a basis for a comparative analysis. The analysis derived the optimal parameters of the kernel and the window function that generates the least MSE. Results showed a higher efficiency in comparison to two or three-parameter convolution kernel

End-to-end performance modeling of passive remote sensing systems

The ultimate goal of end-to-end system modeling is to simulate all known physical effects which determine the content of the data, before flying an instrument system. In remote sensing, one begins with a scene, viewed either statistically or dynamically, computes the radiance in each spectral band, renders the scene, transfers it through representative atmospheres to create the radiance field at an aperture, and integrates over sensor pixels. We have simulated a comprehensive sequence of realistic instrument hardware elements and the transfer of simulated data to an analysis system. This analysis package is the same as that intended for use of data collections from the real system. By comparing the analyzed image to the original scene, the net effect of nonideal system components can be understood. Iteration yields the optimum values of system parameters to achieve performance targets. We have used simulation to develop and test improved multispectral algorithms for (1) the robust retrieval of water surface temperature, water vapor column, and other quantities; (2) the preservation of radiometric accuracy during atmospheric correction and pixel registration on the ground; and (3) exploitation of on-board multispectral measurements to assess the atmosphere between ground and aperture