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In the Eq. (1), f x,y) is the input matrix of pixels representing the N × N sub-image.
To integrate the two membership matrices, ( {varPsi}_{d_H}(f) ) and ( {varPsi}_{d_V}(f) ), we take consider the non-local space information of each pixel, namely, the weight matrix of pixels as follows.
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In these algorithms, each image is represented as a matrix of pixel values.
The spatial sampling was 4.418 × 4.418 mm, and the 2D matrix of pixel was 128 × 128.
For the WBS acquisition, the spatial sampling was 2.209 × 2.209 mm, and the 2D matrix of pixel was 256 × 1024.
In their approach, the distortion is not necessarily an additive function over the pixels because the features may contain higher-order statistics such as sample transition probability matrices of pixels or DCT coefficients modeled as Markov chains [19 21].
It measures 15 mm×30 mm and contains a matrix of 512×1024 pixels with in-pixel amplification, shaping, discrimination and multi-event buffering.
Its main part is a matrix of 40×32 pixels with 100 μm×100 μm pixel size.
This is done by using a spatial neighborhood matrix of 5×5 pixels, centered on each pixel and a one-dimensional weight kernel with the following coefficients: (0.0625,0.25,0.375,0.25,0.0625) [2].
The detector chip contains a 128×128 matrix consisting of pixels of 21×21μm2 -size.
This chip consists of a matrix of 1024 pixels each 135u by 135u, each pixel comprises a CSA, designed to handle signals of few thousand electrons, a shaper, a discriminator and a 10 bit event buffer.
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