Deconvolution Problem
New Regularization Algorithms for Solving the Deconvolution Problem in Well Test Data Interpretation
... pressure-rate deconvolution problem in well test data interpretation, are developed in this ...the problem are the strong nonuniform scale of the solution and large errors (up to 15%) in the input ...
13
Sparse Deconvolution with Applications to Spike Sorting
... this deconvolution approach may also be useful for spike detection in intra- or juxta-cellular recordings, either because we wish to perform spike removal for analysis of subthreshold membrane voltage, or because ...
87
Sparse Deconvolution Using Support Vector Machines
... equivalent problem statement to obtain a sparse ...a deconvolution problem will not be always a valid Mercer kernel, a filtered version of the observations is used, in such a way that the ...
13
Efficient Deconvolution Algorithm
... Efficient Deconvolution Algorithm for ...the deconvolution problem is converted into an easier non-white noise removal problem, at the likely price of losing global ...based ...
8
A Dynamic Tap Allocation for Concurrent CMA-DD Equalizers
... channel deconvolution problem with a specified and reduced number of active taps in the equalizer filter, even when operating under an intense dynamic multipath scenario ( f doppler = 150 ...
6
Advances in phase retrieval and blind deconvolution
... Thus when given a blind deconvolution problem, in which it is known that the convolution components are impulsive, it can be difficult to incorporate this valuable a priori knowledge int[r] ...
204
Deblurring of Noisy or Blurred Image by Using Kernel Estimation Algorithm
... blind deconvolution problem - both blur kernel estimation and image deconvolution is highly ...image deconvolution are occurs even when using a perfect kernel in the reconstructed ...
10
Multiscale inference for multivariate deconvolution
... a deconvolution problem over a H¨ older-β-class is of order n −(β−1)/(2β+2r+1) (β ≥ 2), and it is conjectured that the rate is of order n −(β−1)/(2β+2r+d) in the multivariate ...the problem of ...
41
Revisiting Bayesian Blind Deconvolution
... non-blind deconvolution problem is also harder), the SSD ratio between the image deconvolved with the estimated kernel and the image deconvolved with the ground-truth kernel is used as the final evaluation ...
40
A Robust Orthogonal Adaptive Approach to SISO Deconvolution
... general problem of equalization and particularly adap- tive equalization is well described in [28] and a review with comparisons between recursive and nonrecursive techniques is given in ...general ...
15
Validation of Deconvolution Methodology for SSI Analyses
... The effect of this overstraining was evaluated for a specific deep soil site by statistically comparing the response parameters for (1) a base case which consists of[r] ...
8
Deconvolution of the Error Associated with Random Sampling
... We have confirmed the MPN results in Figure 2 and Figure 4 by showing that the frequency distribution of x + which we have observed in these experiments closely follows Equation 3 compar[r] ...
23
A New Methodology for Blind Image Deconvolution
... The Images are captured by an image detection device such as digital cameras, Charge-Coupled Device (CCD) and mobile phones are regularly different than the actual image perceived by the device user. The main difference ...
5
Convolution and Deconvolution Using Vedic Mathematics
... and Deconvolution is Multiplier and ...For Deconvolution various Divider is Studied, by comparing the advantages and disadvantages of each method, Divider using Paravartya Sutra is used for ...
8
Understanding deep learning via backtracking and deconvolution
... It is a common practice to use visualizations to gain intuition about features that have been learned by a trained ConvNet. Zeiler and Fergus [15] introduced the process of deconvolution as a technique to ...
14
Combined method of Blur Deconvolution and HR image reconstruction using Segment Adaptive Gradient Angle Interpolation (SAGA)
... The proposed blur estimation procedure pre-processes the estimated HR image by applying an edge emphasizing smoothing operation which enhances the soft edges toward step edges while smoothing out weak structures of the ...
5
Deconvolution in real time of noisy signals
... an This presents paper least constrained of the analysis squares filter and a feedback structure is derived which shows the noise cancelling proper— ties of the filter, Using an identifi[r] ...
5
Blind deconvolution with an application to speckle imaging
... 7.5 The object error, the point spread function error, the convolution error and the total error achieved by the conjugate gradient algorithm for different number of frames at 1000 itera[r] ...
211
Development of methods for capillary isoelectric focusing of dairy proteins : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Chemistry at Massey University
... Figure 52 Results of deconvolution of GMP 133 Figure 53 Results of lgG standard infused into MS 134 Figure 54 Results of deconvolution of lgG 134 Figure 55 Results of lactoferrin standar[r] ...
177
Development of methods for capillary isoelectric focusing of dairy proteins : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Chemistry at Massey University
... Figure 52 Results of deconvolution of GMP 133 Figure 53 Results of lgG standard infused into MS 134 Figure 54 Results of deconvolution of lgG 134 Figure 55 Results of lactoferrin standar[r] ...
20