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code based on wiener deconvolution and butterworth filter (MathWorks Inc)

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MathWorks Inc code based on wiener deconvolution and butterworth filter
Code Based On Wiener Deconvolution And Butterworth Filter, supplied by MathWorks Inc, 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/based+deconvolution+filter/pm29368504-35-26-19
Average 90 stars, based on 1 article reviews

code based on wiener deconvolution and butterworth filter - by Bioz Stars, 2026-09

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Article Title: Atypical development of sequential manual motor planning and visuomotor integration in children with autism at early school-age: A longitudinal kinematic study.
Article Snippet: Sensorimotor difficulties are common in children with autism spectrum disorder, and it has been suggested that motor planning problems underlie their atypical movements.. At early school-age, motor planning development typically involves changes in visuomotor integration, a function known to be affected in autism spectrum disorder.. However, there is a lack of detailed characterization of typical motor planning development during this stage, and how motor planning develops in children with autism spectrum disorder is largely unknown.

Article Title: Tracking brain maturation in vivo: functional connectivity, white matter integrity, and synaptic density in developing mice.
Article Snippet: Finally, a bandpass filter (0.01–0.12 Hz) was applied using the Butterworth filter in MATLAB 2021a to preserve low frequency fluctuations in the BOLD signal time course.

Article Title: Spontaneous alternation of place-cell sequences in the open field through spike frequency adaptation.
Article Snippet: This trace was then smoothed using a Butterworth filter (second order with a cutoff frequency of 0.02 samples/s using the butter function in MATLAB) and z-scored.

Article Title: Beta-frequency sensory stimulation enhances gait rhythmicity through strengthened coupling between striatal networks and stepping movement
Article Snippet: Specifically, movement speed trace was first band pass filtered using a Butterworth filter in the delta frequency range (3–4 Hz), and then Morelet wavelets power spectrograms were calculated with the FieldTrip toolbox ( https://www.fieldtriptoolbox.org/ ) for Matlab.

Article Title: Abstract rule learning promotes cognitive flexibility in complex environments across species
Article Snippet: Raw data were first bandpass-filtered between 600–6000 Hz (Butterworth filter using MATLAB function filtfilt ), and at each time point, the median across all channels was subtracted to reduce noise and remove artifacts .

Article Title: Do maximal isometric trunk tasks produce maximum activity in latissimus dorsi?
Article Snippet: EMG signals were high pass filtered (designed 4th order, 10 Hz Butterworth filter; applied two-pass zero-phase method to achieve overall 8th order), rectified, then low pass filtered (designed 4th order, 3 Hz Butterworth filter; applied two-pass zero-phase method to achieve overall 8th order) to provide an EMG linear envelope (EMG-LE) using Matlab (Version 9 R2022b, The MathWorks, Natick, MA).

Article Title: Spontaneous alternation of place-cell sequences in the open field through spike frequency adaptation.
Article Snippet: Position and velocity were smoothed using a Butterworth filter (second order with a cutoff frequency of 0.1 samples/s using the butter function in MATLAB, selected to give reasonable smoothing to the rat’s trajectory).

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Article Title: Motion-unrestricted dynamic electrocardiogram system utilizing imperceptible electronics
Article Snippet: .. 170 The implementation method of the Butterworth filter is as follows: 171 i) A second-order high-pass filter was designed using the butter function in MATLAB 2016a, 172 with the normalized cutoff frequency calculated from a sampling rate of 250 Hz and a cutoff 173 frequency of 0.5 Hz. ..

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Image deconvolution cannot always resolve individual fluorophore locations. (a) A typical yeast mitotic spindle experimental fluorescence image (kinetochore-associated fluorescence, green; spindle pole body fluorescence, red). (b) Theoretical point-source fluorophores (32 green points, representing individual kinetochores, and 2 red points, representing the spindle pole bodies) along a 1500 nm length. The bright green pixels indicate the presence of multiple fluorophores within the pixel area. For simplicity, it was assumed that there are no fluorophores in out-of-focus focal planes. (c) Point-source fluorophores in (a) are convolved with the microscope PSF and noise is added. (d) The image in (b) has been deconvolved using the identical PSF. The image deconvolution process cannot resolve the individual point-source fluorophores and tends to generate fluorescent “clusters” in the periphery which are artifacts of deconvolving noise

Journal: Cellular and Molecular Bioengineering

Article Title: Model Convolution: A Computational Approach to Digital Image Interpretation

doi: 10.1007/s12195-010-0101-7

Figure Lengend Snippet: Image deconvolution cannot always resolve individual fluorophore locations. (a) A typical yeast mitotic spindle experimental fluorescence image (kinetochore-associated fluorescence, green; spindle pole body fluorescence, red). (b) Theoretical point-source fluorophores (32 green points, representing individual kinetochores, and 2 red points, representing the spindle pole bodies) along a 1500 nm length. The bright green pixels indicate the presence of multiple fluorophores within the pixel area. For simplicity, it was assumed that there are no fluorophores in out-of-focus focal planes. (c) Point-source fluorophores in (a) are convolved with the microscope PSF and noise is added. (d) The image in (b) has been deconvolved using the identical PSF. The image deconvolution process cannot resolve the individual point-source fluorophores and tends to generate fluorescent “clusters” in the periphery which are artifacts of deconvolving noise

Article Snippet: Figure 2 High noise levels limit the utility of the image deconvolution method. (a1) A simulated point-source fluorophore has been convolved with a theoretical PSF (no background noise) to produce a 32 × 32 image having a single signal in the center of the field. (a2) Subsequent image deconvolution (by Wiener filtering-based deconvolution using the Matlab image processing toolbox) precisely resolves the spreading of light due to the PSF, and correctly identifies the fluorophore location to be at the center. (b1) A simulated point-source fluorophore has been convolved with a theoretical PSF, but noise has been added to the image such that the SNR = 8. (b2) In this case, subsequent image deconvolution is able to correctly resolve the fluorophore location. (b3) In another image with SNR = 8, image deconvolution is not able to separate the fluorophore from background noise and misidentifies the location of the point source. (c) The ability of Wiener-filter-based deconvolution to separate fluorophores from background noise decreases substantially with decreasing SNR.

Techniques: Fluorescence, Microscopy

High noise levels limit the utility of the image deconvolution method. (a1) A simulated point-source fluorophore has been convolved with a theoretical PSF (no background noise) to produce a 32 × 32 image having a single signal in the center of the field. (a2) Subsequent image deconvolution (by Wiener filtering-based deconvolution using the Matlab image processing toolbox) precisely resolves the spreading of light due to the PSF, and correctly identifies the fluorophore location to be at the center. (b1) A simulated point-source fluorophore has been convolved with a theoretical PSF, but noise has been added to the image such that the SNR = 8. (b2) In this case, subsequent image deconvolution is able to correctly resolve the fluorophore location. (b3) In another image with SNR = 8, image deconvolution is not able to separate the fluorophore from background noise and misidentifies the location of the point source. (c) The ability of Wiener-filter-based deconvolution to separate fluorophores from background noise decreases substantially with decreasing SNR. The quantitative relationship between the failure rate and the SNR depends upon the specifics of the problem, but generally failure rate increases with decreasing SNR

Journal: Cellular and Molecular Bioengineering

Article Title: Model Convolution: A Computational Approach to Digital Image Interpretation

doi: 10.1007/s12195-010-0101-7

Figure Lengend Snippet: High noise levels limit the utility of the image deconvolution method. (a1) A simulated point-source fluorophore has been convolved with a theoretical PSF (no background noise) to produce a 32 × 32 image having a single signal in the center of the field. (a2) Subsequent image deconvolution (by Wiener filtering-based deconvolution using the Matlab image processing toolbox) precisely resolves the spreading of light due to the PSF, and correctly identifies the fluorophore location to be at the center. (b1) A simulated point-source fluorophore has been convolved with a theoretical PSF, but noise has been added to the image such that the SNR = 8. (b2) In this case, subsequent image deconvolution is able to correctly resolve the fluorophore location. (b3) In another image with SNR = 8, image deconvolution is not able to separate the fluorophore from background noise and misidentifies the location of the point source. (c) The ability of Wiener-filter-based deconvolution to separate fluorophores from background noise decreases substantially with decreasing SNR. The quantitative relationship between the failure rate and the SNR depends upon the specifics of the problem, but generally failure rate increases with decreasing SNR

Techniques:

The model-convolution method as compared to the image deconvolution process. In the image deconvolution process, an experimental image is “deblurred” using the theoretical microscope PSF. With the model-convolution method, a theoretical fluorophore distribution is convolved with the microscope PSF and noise, and a simulated image is generated. Thus, the model-convolution method is essentially the inverse of the image deconvolution process. (a) The example shown is a computational model of Arp2/3-mediated actin filament branching in three dimensions based on experimental observations by Ichetovkin et al . The model results in a branched actin filament structure stemming from an initial nucleation site (1—blue arrow) and leading to a series of branches off the main filament. The model-convolution method is applied to create a theoretical microscope image at the focal plane of the main filament. Branches that are close to the focal plane of the microscope (2—orange arrow) are clearly visible in the simulated fluorescence image. Branches that project out of the focal plane (3—red arrow, and 4—white arrow) are less visible in the simulated image, indicating that the branching complexity and branch length distribution of the actin filament could be misinterpreted from experimental fluorescence images. Scale bar, 1000 nm. (b) The model-convolution approach to estimating microtubule curvature. A simulated microtubule is constructed with a known analytical function (Sine function on a 2 nm pixel grid), showing the true underlying relation of the curvature to the outer diameter. This simulated microtubule has curvature that would be at the high extreme of observed curvatures in living cells. The model-convolution operation is performed, and the resulting image is binned to the pixel size associated with a high NA lens and ccd detector (50 nm pixel size). The convolved image appears more highly curved than the underlying filament, and the digitization on the camera makes quantitative analysis of curvature prone to errors. Scale bar, 250 nm

Journal: Cellular and Molecular Bioengineering

Article Title: Model Convolution: A Computational Approach to Digital Image Interpretation

doi: 10.1007/s12195-010-0101-7

Figure Lengend Snippet: The model-convolution method as compared to the image deconvolution process. In the image deconvolution process, an experimental image is “deblurred” using the theoretical microscope PSF. With the model-convolution method, a theoretical fluorophore distribution is convolved with the microscope PSF and noise, and a simulated image is generated. Thus, the model-convolution method is essentially the inverse of the image deconvolution process. (a) The example shown is a computational model of Arp2/3-mediated actin filament branching in three dimensions based on experimental observations by Ichetovkin et al . The model results in a branched actin filament structure stemming from an initial nucleation site (1—blue arrow) and leading to a series of branches off the main filament. The model-convolution method is applied to create a theoretical microscope image at the focal plane of the main filament. Branches that are close to the focal plane of the microscope (2—orange arrow) are clearly visible in the simulated fluorescence image. Branches that project out of the focal plane (3—red arrow, and 4—white arrow) are less visible in the simulated image, indicating that the branching complexity and branch length distribution of the actin filament could be misinterpreted from experimental fluorescence images. Scale bar, 1000 nm. (b) The model-convolution approach to estimating microtubule curvature. A simulated microtubule is constructed with a known analytical function (Sine function on a 2 nm pixel grid), showing the true underlying relation of the curvature to the outer diameter. This simulated microtubule has curvature that would be at the high extreme of observed curvatures in living cells. The model-convolution operation is performed, and the resulting image is binned to the pixel size associated with a high NA lens and ccd detector (50 nm pixel size). The convolved image appears more highly curved than the underlying filament, and the digitization on the camera makes quantitative analysis of curvature prone to errors. Scale bar, 250 nm

Techniques: Microscopy, Generated, Fluorescence, Construct

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