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It has been stated here : https://phys.org/news/2019-07-quantum-technology.html that the the so-called Kravchuk transform is very important in the field of image processing and possibly in signal processing in general.

I can hardly find any description about this (e.g. not mentioned in Wikipedia, etc.).

It seems to be mentioned in this paper for example:
https://www.researchgate.net/publication/24147178_Mode_analysis_and_signal_restoration_with_Kravchuk_functions

Transliterations of Ukrainian names have different avatars. You can find Kravchuk polynomials, and other papers like On Krawtchouk Transforms or Krawtchouk polynomials and Krawtchouk matrices. You can find as well Kravchuk orthogonal polynomials.

As they form an orthogonal basis of polynomials (as well as many others, listed on polpak: Bernoulli, Bernstein, Chebyshev, Hermite, Laguerre, Legendre, Zernike), they are candidates for a transform. Derived moments are used in image processing, and the following paper seems to have a wide audience:

• 
  Image analysis by Krawtchouk moments, IEEE Transactions on Image Processing 2003

A new set of orthogonal moments based on the discrete classical
Krawtchouk polynomials is introduced. The Krawtchouk polynomials are
scaled to ensure numerical stability, thus creating a set of weighted
Krawtchouk polynomials. The set of proposed Krawtchouk moments is then
derived from the weighted Krawtchouk polynomials. The orthogonality of
the proposed moments ensures minimal information redundancy. No
numerical approximation is involved in deriving the moments, since the
weighted Krawtchouk polynomials are discrete. These properties make
the Krawtchouk moments well suited as pattern features in the analysis
of two-dimensional images. It is shown that the Krawtchouk moments can
be employed to extract local features of an image, unlike other
orthogonal moments, which generally capture the global features. The
computational aspects of the moments using the recursive and symmetry
properties are discussed. The theoretical framework is validated by an
experiment on image reconstruction using Krawtchouk moments and the
results are compared to that of Zernike, pseudo-Zernike, Legendre, and
Tchebyscheff moments. Krawtchouk moment invariants are constructed
using a linear combination of geometric moment invariants; an object
recognition experiment shows Krawtchouk moment invariants perform
significantly better than Hu's moment invariants in both noise-free
and noisy conditions.

Later, you can read:

• 
  Image Analysis Using Hahn Moments, IEEE Transactions on Pattern Analysis and Machine, 2007, where Hahn moments generalize Chebyshev and Krawtchouk moments:

This paper shows how Hahn moments provide a unified understanding of
the recently introduced Chebyshev and Krawtchouk moments. The two
latter moments can be obtained as particular cases of Hahn moments
with the appropriate parameter settings and this fact implies that
Hahn moments encompass all their properties. The aim of this paper is
twofold: (1) To show how Hahn moments, as a generalization of
Chebyshev and Krawtchouk moments, can be used for global and local
feature extraction and (2) to show how Hahn moments can be
incorporated into the framework of normalized convolution to analyze
local structures of irregularly sampled signals.

In Wikipedia's Discrete Fourier transform we find:

The choice of eigenvectors of the DFT matrix has become important in
recent years in order to define a discrete analogue of the fractional
Fourier transform—the DFT matrix can be taken to fractional powers by
exponentiating the eigenvalues (e.g., Rubio and Santhanam, 2005). For
the continuous Fourier transform, the natural orthogonal
eigenfunctions are the Hermite functions, so various discrete
analogues of these have been employed as the eigenvectors of the DFT,
such as the Kravchuk polynomials (Atakishiyev and Wolf, 1997). The
"best" choice of eigenvectors to define a fractional discrete Fourier
transform remains an open question, however.