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Everything about Deconvolution
Reconstruction of a filtered signal
In mathematics, deconvolution is the inverse of convolution. Both operations are used in signal processing and image processing. For example, it may be possible to recover the original signal after a filter (convolution) by using a deconvolution method with a certain degree of accuracy. Due to the measurement error of the recorded signal or image, it can be demonstrated that the worse the signal-to-noise ratio (SNR), the worse the reversing of a filter will be; hence, inverting a filter is not always a good solution as the error amplifies. Deconvolution offers a solution to this problem. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: DeconvolutionA 5-minute orientation
- Skim a recent paper: Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphSTInfluential work since 2022
- Pick up a classic book: Advances in anisotropyThe most-reprinted book on the subject
- Read the landmark paper: An Information-Maximization Approach to Blind Separation and Blind DeconvolutionCited 7,417 times
- Go sideways: Wiener deconvolutionA closely related topic
- Go sideways: Blind deconvolutionA closely related topic
📖 Definitions
Noun The inversion of a convolution equation; does not normally have unique solution
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis
Influence of the Discretization Methods on the Distribution of Relaxation Times Deconvolution: Implementing Radial Basis Functions with DRTtools
NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data
Robust determination of the fibre orientation distribution in diffusion MRI: Non-negativity constrained super-resolved spherical deconvolution
Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data
Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution
Magnetic interpretation in three dimensions using Euler deconvolution
🆕 Influential since 2022
Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST
Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology
Spatially informed cell-type deconvolution for spatial transcriptomics
Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution
Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data
📚 Books worth reading
Advances in anisotropyInternational Workshop on Seismic Anisotropy (7th 1996 Miami, Fla.) · 1978
Blind image deconvolutionPatrizio Campisi, Karen Egiazarian · 2007
Seismic Inversion and DeconvolutionEnders A. Robinson, E.A. Robinson · 1984
Deconvolution of Images and SpectraPeter A. Jansson · 1996
Maximum-Likelihood DeconvolutionJerry M. Mendel · 1989- 📕Seĭsmoakusticheskie issledovanii͡a︡ na akvatorii͡a︡khKalinin, A. V. · 1983
Deconvolution and inverse theoryVijay Dimri · 1992
Seismic inverse Q filteringYanghua Wang · 2008
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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