Problems in machine vision that are posed as variational principles or partial differential equations can often be solved by local, iterative, and parallel algorithms. A disadvantage of these algorithms is that they are inefficient at propagating constraints across large visual representations. Application of multigrid methods has overcome this drawback with regard to the computation of visible-surface representations. We argue that our multiresolution approach has wide applicability in vision. In particular, we describe efficient multiresolution iterative algorithms for computing lightness, shape-from-shading, and optical flow, and evaluate the performance of these algorithms using synthesized images.