slow imwarp with large arrays

Hi @Nic Bac ,

As mentioned in the documentation,

https://www.mathworks.com/help/images/ref/imwarp.html

specifying the OutputView parameter can enhance performance by defining the output size and location. For large images, defining an appropriate output view can minimize unnecessary computations and speed up processing:

   outputView = affineOutputView(size(refimg), tform);
   [Imagenew, Rnew] = imwarp(refimg, Rimage, tform, "OutputView", 
    outputView, "cubic", FillValues=0);

If performance is still an issue, consider using other functions or techniques that may better utilize multi-core capabilities. For example, if you have access to a compatible GPU, leveraging GPU computing could significantly speed up your image transformations:

     refimgGPU = gpuArray(refimg);
     [ImagenewGPU, Rnew] = imwarp(refimgGPU, RimageGPU, tform, "cubic", 
     FillValues=0);
     Imagenew = gather(ImagenewGPU); % Transfer back to CPU

For extremely large images or when dealing with multiple images, consider breaking down the image into smaller tiles or batches and processing them individually in parallel:

   % Example of tiling approach (pseudo-code)
   parfor i = 1:numTiles
       [Imagenew{i}, Rnew{i}] = imwarp(tiles{i}, RimageTile{i}, tform);
   end

Please bear in mind that large images require significant memory resources. Ensure your system has enough RAM to handle the image sizes you are working with and using a recent version of MATLAB that supports advanced features in parallel computing and GPU acceleration. Versions beyond R2021a have improved support for these capabilities. Also, utilize MATLAB's built-in profiler (profile on; ...; profile viewer;) to identify bottlenecks in your code execution and determine whether imwarp is indeed the limiting factor.

Hope this helps.