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naïve inverse filter  (Neuroscience Information Framework)

 
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    Neuroscience Information Framework naïve inverse filter
    Orthogonal sections of the maximum intensity projection (MIP) of a degraded 3D synthetic volume after deconvolution using various algorithms. From top left to bottom right: ground-truth volume, degraded volume (after convolution with PSF and simulate noise), <t>naïve</t> inverse <t>filter</t> <t>(NIF),</t> regularized inverse Filter (RIF), Tikhonov regularization (TR), Landweber iteration (LW), Richardson–Lucy (RL), Tikhonov–Miller (TM), Fast Iterative Shrinkage-Thresholding Algorithm (FISTA, l 1 minimization), and Richardson–Lucy with total variation (RL-TV). A non-negativity constraint was used for all algorithms. The setting of the optimal parameters for each deconvolution algorithm was performed through visual assessment. Reprinted from with permission from ref . Copyright 2017 Elsevier.
    Naïve Inverse Filter, supplied by Neuroscience Information Framework, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/na%C3%AFve+inverse+filter/pmc11912138-188-4-11?v=Neuroscience+Information+Framework
    Average 90 stars, based on 1 article reviews
    naïve inverse filter - by Bioz Stars, 2026-08
    90/100 stars

    Images

    1) Product Images from "Computational Super-Resolution: An Odyssey in Harnessing Priors to Enhance Optical Microscopy Resolution"

    Article Title: Computational Super-Resolution: An Odyssey in Harnessing Priors to Enhance Optical Microscopy Resolution

    Journal: Analytical Chemistry

    doi: 10.1021/acs.analchem.4c07047

    Orthogonal sections of the maximum intensity projection (MIP) of a degraded 3D synthetic volume after deconvolution using various algorithms. From top left to bottom right: ground-truth volume, degraded volume (after convolution with PSF and simulate noise), naïve inverse filter (NIF), regularized inverse Filter (RIF), Tikhonov regularization (TR), Landweber iteration (LW), Richardson–Lucy (RL), Tikhonov–Miller (TM), Fast Iterative Shrinkage-Thresholding Algorithm (FISTA, l 1 minimization), and Richardson–Lucy with total variation (RL-TV). A non-negativity constraint was used for all algorithms. The setting of the optimal parameters for each deconvolution algorithm was performed through visual assessment. Reprinted from with permission from ref . Copyright 2017 Elsevier.
    Figure Legend Snippet: Orthogonal sections of the maximum intensity projection (MIP) of a degraded 3D synthetic volume after deconvolution using various algorithms. From top left to bottom right: ground-truth volume, degraded volume (after convolution with PSF and simulate noise), naïve inverse filter (NIF), regularized inverse Filter (RIF), Tikhonov regularization (TR), Landweber iteration (LW), Richardson–Lucy (RL), Tikhonov–Miller (TM), Fast Iterative Shrinkage-Thresholding Algorithm (FISTA, l 1 minimization), and Richardson–Lucy with total variation (RL-TV). A non-negativity constraint was used for all algorithms. The setting of the optimal parameters for each deconvolution algorithm was performed through visual assessment. Reprinted from with permission from ref . Copyright 2017 Elsevier.

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    Similar Products

    naïve inverse filter  (Neuroscience Information Framework)
    90
    Neuroscience Information Framework naïve inverse filter
    Orthogonal sections of the maximum intensity projection (MIP) of a degraded 3D synthetic volume after deconvolution using various algorithms. From top left to bottom right: ground-truth volume, degraded volume (after convolution with PSF and simulate noise), <t>naïve</t> inverse <t>filter</t> <t>(NIF),</t> regularized inverse Filter (RIF), Tikhonov regularization (TR), Landweber iteration (LW), Richardson–Lucy (RL), Tikhonov–Miller (TM), Fast Iterative Shrinkage-Thresholding Algorithm (FISTA, l 1 minimization), and Richardson–Lucy with total variation (RL-TV). A non-negativity constraint was used for all algorithms. The setting of the optimal parameters for each deconvolution algorithm was performed through visual assessment. Reprinted from with permission from ref . Copyright 2017 Elsevier.
    Naïve Inverse Filter, supplied by Neuroscience Information Framework, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/na%C3%AFve+inverse+filter/pmc11912138-188-4-11?v=Neuroscience+Information+Framework
    Average 90 stars, based on 1 article reviews
    naïve inverse filter - by Bioz Stars, 2026-08
    90/100 stars
    		  Buy from Supplier
    naïve inverse filtering  (Neuroscience Information Framework)
    90
    Neuroscience Information Framework naïve inverse filtering
    Orthogonal sections of the maximum intensity projection (MIP) of a degraded 3D synthetic volume after deconvolution using various algorithms. From top left to bottom right: ground-truth volume, degraded volume (after convolution with PSF and simulate noise), <t>naïve</t> inverse <t>filter</t> <t>(NIF),</t> regularized inverse Filter (RIF), Tikhonov regularization (TR), Landweber iteration (LW), Richardson–Lucy (RL), Tikhonov–Miller (TM), Fast Iterative Shrinkage-Thresholding Algorithm (FISTA, l 1 minimization), and Richardson–Lucy with total variation (RL-TV). A non-negativity constraint was used for all algorithms. The setting of the optimal parameters for each deconvolution algorithm was performed through visual assessment. Reprinted from with permission from ref . Copyright 2017 Elsevier.
    Naïve Inverse Filtering, supplied by Neuroscience Information Framework, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/na%C3%AFve+inverse+filter/10__1109_slash_access__2020__3040319-58-5-8?v=Neuroscience+Information+Framework
    Average 90 stars, based on 1 article reviews
    naïve inverse filtering - by Bioz Stars, 2026-08
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Orthogonal sections of the maximum intensity projection (MIP) of a degraded 3D synthetic volume after deconvolution using various algorithms. From top left to bottom right: ground-truth volume, degraded volume (after convolution with PSF and simulate noise), naïve inverse filter (NIF), regularized inverse Filter (RIF), Tikhonov regularization (TR), Landweber iteration (LW), Richardson–Lucy (RL), Tikhonov–Miller (TM), Fast Iterative Shrinkage-Thresholding Algorithm (FISTA, l 1 minimization), and Richardson–Lucy with total variation (RL-TV). A non-negativity constraint was used for all algorithms. The setting of the optimal parameters for each deconvolution algorithm was performed through visual assessment. Reprinted from with permission from ref . Copyright 2017 Elsevier.

    Journal: Analytical Chemistry

    Article Title: Computational Super-Resolution: An Odyssey in Harnessing Priors to Enhance Optical Microscopy Resolution

    doi: 10.1021/acs.analchem.4c07047

    Figure Lengend Snippet: Orthogonal sections of the maximum intensity projection (MIP) of a degraded 3D synthetic volume after deconvolution using various algorithms. From top left to bottom right: ground-truth volume, degraded volume (after convolution with PSF and simulate noise), naïve inverse filter (NIF), regularized inverse Filter (RIF), Tikhonov regularization (TR), Landweber iteration (LW), Richardson–Lucy (RL), Tikhonov–Miller (TM), Fast Iterative Shrinkage-Thresholding Algorithm (FISTA, l 1 minimization), and Richardson–Lucy with total variation (RL-TV). A non-negativity constraint was used for all algorithms. The setting of the optimal parameters for each deconvolution algorithm was performed through visual assessment. Reprinted from with permission from ref . Copyright 2017 Elsevier.

    Article Snippet: The performance of the naïve inverse filter is shown in Figure (NIF), where the result is full of noise.

    Techniques: