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imnoise function in matlab 2020b  (MathWorks Inc)


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    MathWorks Inc imnoise function in matlab 2020b
    Imnoise Function In Matlab 2020b, 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/matlab+function+'imnoise/pm38830365-100-48-51
    Average 90 stars, based on 1 article reviews
    imnoise function in matlab 2020b - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: Effects of expectation on face perception and its association with expertise.
    Article Snippet: This was done using the MATLAB function ‘imnoise’, with a density of 0.75 and ‘salt & pepper’ type of noise (on and off pixels).

    Article Title: Nanoscale Distribution of Nuclear Sites by Super-Resolved Image Cross-Correlation Spectroscopy
    Article Snippet: Poisson noise was introduced to each pixel at a level based on the pixel value using the MATLAB function ‘‘imnoise.’’ To simulate the uncorrelated sample, the foci were distributed randomly in both channels.

    Article Title: Phase grouping-based needle segmentation in 3-D trans-rectal ultrasound-guided prostate trans-perineal therapy.
    Article Snippet: A Engine sity of qiu.wu Abstract—A robust and efficient needle segmentation method used to localize and track the needle in 3-D transrectal ultrasound (TRUS)-guided prostate therapy is proposed.. The algorithmic procedure begins by cropping the 3-D US image containing a needle; then all voxels in the cropped 3-D image are grouped into different line support regions (LSRs) based on the outer product of the adjacent voxels’ gradient vector.. Two different needle axis extractionmethods in the candidate LSR are presented: least-squares fitting and 3-D randomizedHough transform.

    Article Title: 3D Superalloy Grain Segmentation Using a Multichannel Edge-Weighted Centroidal Voronoi Tessellation Algorithm
    Article Snippet: Accurate grain segmentation on 3D superalloy images is very important in materials science and engineering.. From grain segmentation, we can derive the underlying superalloy grains’ micro-structures, based on which many important physical, mechanical and chemical properties of the superalloy samples can be evaluated.. However, grain segmentation is usually a very challenging problem due to: 1) even a small 3D superalloy sample may contain hundreds of grains; 2) carbides and noises may degrade the imaging quality; and 3) the intensity within a grain may not be homogeneous.

    Imaging:

    Article Title: Optimized spectral filter design enables more accurate estimation of oxygen saturation in spectral imaging
    Article Snippet: .. For simulated imaging, a set of i = 1, ..., n spectral filters are defined as, (8) F = [ F 1 ( λ ) ⋯ F n ( λ ) ] where, (9) F i ( λ ) = N exp [ − 4 ln ( 2 ) ( λ − λ c , i w i ) 2 ] where N is a normalization factor that ensures the area under the curve is equal to 1, λ c,i is the center wavelength of the i th filter, and w i is the full width half maximum (FWHM) of the i th filter such that, (10) λ c = [ λ c , 1 ⋯ λ c , n ] (11) w = [ w 1 ⋯ w n ] are the center wavelengths and FWHMs of the filter set F . To simulate imaging with the filter set, the signal hypercube, S(x,y,λ) , an image cube is calculated, (12) I ( x , y ) = [ I 1 ( x , y ) ⋯ I n ( x , y ) ] where, (13) I i ( x , y ) = ∫ − ∞ + ∞ S ( x , y , λ ) F i ( λ ) d λ Zero-mean gaussian white noise was added to each image using the MATLAB function ‘imnoise’ with variance 5 × 10 −5 . ..



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