Search results for "Deconvolution"

On the SN 1993J Radio Shell Structure

2005

An accurate measurement of the expansion deceleration of SN 1993J depends on how well the shell size and its emission structure are known. With the goal of determining the emission structure of the shell, we have developed a new approach, which we call “Green Function Deconvolution” (GFD), based on iterative use of Green functions on the sky plane to reconstruct the radial emission profiles of spherically symmetric sources. This approach works reasonably well in the case of optically thin emitting sources, which is not the case for SN 1993J since, as we find, the emission from the central part of SN 1993J further away from us is strongly or totally absorbed. We describe the GFD method and p…

Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

2016

This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images). Our paper addresses this efficiency issue. Instead of a numerical deconvolution in previous work, we precompute a feed-forward, strided convolutional network that captures the feature statistics of Markovian patches and is able to directly generate outputs of arbitrary dimensions. Such network can directly decode brown noise to realistic textu…

2021

Abstract We propose a signal deconvolution procedure for imaging spectrometer data, where a measured point spread function (PSF) is deconvolved itself before being used for deconvolution of the signal. We evaluate the effectiveness of our procedure for improvement of the spatio-spectral signal, as well as our target application, i.e. estimation of sun-induced fluorescence (SIF). Imaging spectrometers are well established instruments for remote sensing. When used for scientific purposes these instruments are usually calibrated on a regular basis. In our case the point spread function of the optics is measured in an elaborate procedure with a tunable monochromator point light source. PSFs are…

A Model for the Description, Simulation, and Deconvolution of Skewed Chromatographic Peaks

1997

A family of models is proposed for the description of skewed chromatographic peaks, based on the modification of the standard deviation of a pure Gaussian peak, by the use of a polynomial function, h(t) = He-(1/2)([t-tR]/[s0+s1(t-tR)+s2(t-tR)2+...])2, where H and tR are the height and time at the peak maximum, respectively. The model has demonstrated a high flexibility with peaks of a wide range of asymmetry and can be used to accurately predict the profile of asymmetrical peaks, using the values of efficiency and asymmetry factor measured on experimental chromatograms. This possibility permits the simulation of chromatograms and the optimization of the separation of mixtures of compounds p…

Deconvolution filtering for nonlinear stochastic systems with randomly occurring sensor delays via probability-dependent method

2013

This paper deals with a robustH∞deconvolution filtering problem for discrete-time nonlinear stochastic systems with randomly occurring sensor delays. The delayed measurements are assumed to occur in a random way characterized by a random variable sequence following the Bernoulli distribution with time-varying probability. The purpose is to design anH∞deconvolution filter such that, for all the admissible randomly occurring sensor delays, nonlinear disturbances, and external noises, the input signal distorted by the transmission channel could be recovered to a specified extent. By utilizing the constructed Lyapunov functional relying on the time-varying probability parameters, the desired su…

Depth-of-Field Enhancement in Integral Imaging by Selective Depth-Deconvolution

2014

One of the major drawbacks of the integral imaging technique is its limited depth of field. Such limitation is imposed by the numerical aperture of the microlenses. In this paper, we propose a method to extend the depth of field of integral imaging systems in the reconstruction stage. The method is based on the combination of deconvolution tools and depth filtering of each elemental image using disparity map information. We demonstrate our proposal presenting digital reconstructions of a 3-D scene focused at different depths with extended depth of field.

Introduction to Digital Signal Processing

2018

Signal processing deals with the representation, transformation, and manipulation of signals and the information they contain. Typical examples include extracting the pure signals from a mixture observation (a field commonly known as deconvolution) or particular signal (frequency) components from noisy observations (generally known as filtering). This chapter outlines the basics of signal processing and then introduces the more advanced concepts of time‐frequency and time‐scale representations, as well as emerging fields of compressed sensing and multidimensional signal processing. When moving to multidimensional signal processing, a modern approach is taken from the point of view of statis…

Deconvolution of XPS spectra

1991

The resolution of XPS spectra is limited mainly by instrumental parameters like the spectral line width of the exciting X-ray source and the finite energy resolution of the electron analyzer. If the line broadening functions resulting from the instrumental setup can be estimated and expressed by a spectrometer function, a mathematical recalculation of the intrinsic signal is possible by deconvolution. With the method presented in this paper, a resolution enhancement by a factor of 3 can be obtained. Measured spectra of physically correlated spin orbit doublets have been deconvoluted, and it is shown, that the intensity ratios and the positions are comparable with results obtained by highly …

TheINTEGRALspectrometer SPI: performance of point-source data analysis

2005

The performance of the SPI point-source data analysis system is assessed using a combination of simulations and of observations gathered during the first year of INTEGRAL operations. External error estimates are derived by comparing source positions and fluxes obtained from independent analyses. When the source detection significance provided by the SPIROS imaging reconstruction program increases from ∼10 to ∼100, the errors decrease as the inverse of the detection significance, with values from ∼10 to ∼1 arcmin in positions, and from ∼10 to ∼1 per cent in relative flux. These errors are dominated by Poisson counting noise. Our error estimates are consistent with those provided by the SPIRO…

Block Based Deconvolution Algorithm Using Spline Wavelet Packets

2010

This paper presents robust algorithms to deconvolve discrete noised signals and images. The idea behind the algorithms is to solve the convolution equation separately in different frequency bands. This is achieved by using spline wavelet packets. The solutions are derived as linear combinations of the wavelet packets that minimize some parameterized quadratic functionals. Parameters choice, which is performed automatically, determines the trade-off between the solution regularity and the initial data approximation. This technique, which id called Spline Harmonic Analysis, provides a unified computational scheme for the design of orthonormal spline wavelet packets, fast implementation of the…