Topic
Wiener deconvolution
About: Wiener deconvolution is a research topic. Over the lifetime, 1599 publications have been published within this topic receiving 27195 citations.
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TL;DR: Deconvolution may be effective in diminishing the hemodynamically imposed temporal blurring and may have potential applications in quantitating responses in eventrelated fMRI.
1,343 citations
TL;DR: In this article, an iterative, time-domain deconvolution approach to receiver-function estimation is described and illustrated using synthetic and observation-based examples, which is commonly used in earthquake time-function studies.
Abstract: We describe and apply an iterative, time-domain deconvolution approach to receiver-function estimation and illustrate the reliability and advantages of the technique using synthetic- and observation-based examples. The iterative technique is commonly used in earthquake time-function studies and offers several advantages in receiver-function analysis such as intuitively stripping the largest receiver-function arrivals from the observed seismograms first and then the details; long-period stability by a priori constructing the deconvolution as a sum of Gaussian pulses; and easy generalization to allow multiwaveform deconvolution for a single receiver-function estimate.
1,113 citations
20 Jun 2011
TL;DR: A new type of image regularization which gives lowest cost for the true sharp image is introduced, which allows a very simple cost formulation to be used for the blind deconvolution model, obviating the need for additional methods.
Abstract: Blind image deconvolution is an ill-posed problem that requires regularization to solve. However, many common forms of image prior used in this setting have a major drawback in that the minimum of the resulting cost function does not correspond to the true sharp solution. Accordingly, a range of additional methods are needed to yield good results (Bayesian methods, adaptive cost functions, alpha-matte extraction and edge localization). In this paper we introduce a new type of image regularization which gives lowest cost for the true sharp image. This allows a very simple cost formulation to be used for the blind deconvolution model, obviating the need for additional methods. Due to its simplicity the algorithm is fast and very robust. We demonstrate our method on real images with both spatially invariant and spatially varying blur.
1,054 citations
TL;DR: It is demonstrated that the cross-spectral metric is optimal in the sense that it maximizes mutual information between the observed and desired processes and is capable of outperforming the more complex eigendecomposition-based methods.
Abstract: The Wiener filter is analyzed for stationary complex Gaussian signals from an information theoretic point of view. A dual-port analysis of the Wiener filter leads to a decomposition based on orthogonal projections and results in a new multistage method for implementing the Wiener filter using a nested chain of scalar Wiener filters. This new representation of the Wiener filter provides the capability to perform an information-theoretic analysis of previous, basis-dependent, reduced-rank Wiener filters. This analysis demonstrates that the cross-spectral metric is optimal in the sense that it maximizes mutual information between the observed and desired processes. A new reduced-rank Wiener filter is developed based on this new structure which evolves a basis using successive projections of the desired signal onto orthogonal, lower dimensional subspaces. The performance is evaluated using a comparative computer analysis model and it is demonstrated that the low-complexity multistage reduced-rank Wiener filter is capable of outperforming the more complex eigendecomposition-based methods.
847 citations
684 citations