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where N denotes image pixel, R is radial component in spherical coordinates.
where denotes image height; represents the number of quantization steps; denotes the weight of top-most pixels.
where I denotes image intensity, B denotes the corresponding perceived brightness, is the threshold for intensity, is the maximum value of intensity, and is the maximum value of perceived brightness [23].
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Here, W and H denote image width and height in pixels, respectively.
Notations ℓ 2 and ℓ 2 are used to denote image spaces defined on Ω and Λ, respectively.
Let us denote image motion in a pixel (x,y) at time t by Δ v t) and Δ u(t).
We denote image labels as y, the Fisher Vectors or VLAD values in the ith dimension as x : i, and the mutual information as I(x : i, y).
If we denote image (b) to be a low-rank matrix A, then image (c) is the observed data matrix D which is perturbed by a sparse matrix E, that is, begin{aligned} text{image (c) $=$ image (b)} + E. end{aligned} Figure 1 Cameraman.
Simply, we shall denote images as vectors in R n by concatenating their columns.
where is the value of thei th pixel, denotes the image width, and denotes the image height.
where let matrix [y 1,...., y M ] T denotes the image with monitored noisy, let matrix [x 1, ⋯, x M ] T denotes the image with no noise interference.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com