Coarse-fine stitching strategy
Although the sparse technique and block-wise QR decomposition help to save time and memory, it is still time consuming for the iterative stitching algorithm to converge to an acceptable tolerance because a huge number of measuring points are processed. We propose the coarse-fine stitching strategy to handle this problem. First, the algorithm is applied to the under-sampled subaperture data and gets the suboptimal parameters of configuration, defocus, and so on. This process is not time consuming because low-resolution data are processed. The suboptimal parameters are then used as initials for stitching of the original high-resolution measurements, which usually takes 1 to 2 iterations. Consequently, the coarse- fine stitching strategy significantly reduces the computation time and makes it possible to apply the algorithm in engineering practices. In fact, we can view the coarse stitching as a high-precision motion platform for subaperture alignment. It ensures fast convergence while avoiding costly hardware.