%INPAINT2  The function implements different single-image inpainting algorithms
%
%     dst = cv.inpaint2(src, mask)
%     dst = cv.inpaint2(src, mask, 'OptionName', optionValue, ...)
%
% ## Input
% * __src__ source image, it could be of any type (8/16/32-bit integers or
%   32/64-bit floating-points) and any number of channels from 1 to 4. In case
%   of 3- and 4-channels images the function expect them in CIELab colorspace
%   or similar one, where first color component shows intensity, while second
%   and third shows colors. Nonetheless you can try any colorspaces.
% * __mask__ mask (8-bit 1-channel of same size as `src`), where non-zero
%   pixels indicate valid image area, while zero pixels indicate area to be
%   inpainted.
%
% ## Output
% * __dst__ Output image with the same size and type as `src`.
%
% ## Options
% * __Method__ Inpainting algorithms, one of:
%   * __ShiftMap__ (default) This algorithm searches for dominant
%     correspondences (transformations) of image patches and tries to
%     seamlessly fill-in the area to be inpainted using this transformations.
%
% The function reconstructs the selected image area from known area.
% See the original paper [He2012] for details.
%
% ## References
% [He2012]:
% > Kaiming He, Jian Sun. "Statistics of patch offsets for image completion".
% > In Computer Vision-ECCV 2012, pages 16-29. Springer, 2012.
%
% See also: cv.inpaint
%
