I'm taking your question without context, so sorry if it doesn't make sense: Warping images to match based on structure / features is common in much of image pr

I'm taking your question without context, so sorry if it doesn't make sense: Warping images to match based on structure / features is common in much of image processing and requires no NN since we know how to do it. (But may be enhanced by NN). The basic idea is: 1. Find features in images A and B (SURF / SIFT are common). However, maybe a NN could find interesting features here? 2. Match features so you know what feature from A is where in B. Again, a NN could potentially do this "better". 3. Compute a homography (translation between planes of features) or procrustes transform (or other types of transformations) 4. For each pixel (p) in the target frame (B), take the inverse of the transformation to find the pixel of the original frame (A) (which is usually a fractional pixel because it won't line up) 5. interpolate between nearby pixels in (A) to get the "true" pixel value at (p) in (B). Perhaps a NN could improve this interpolation? We've seen this in super resolution, for example. You can easily reproduce these steps with just a few function calls in OpenCV. Try it out. https://opencv.org/ https://opencv.org/ Up to step 3 is very helpful to answer questions like: "How much did the camera move between captures of image A and B. So now you can solve structure from motion, visual odometry, etc. This is typically how quadrotors navigate with cameras.