The filter flow problem is to compute a space-variant linear filter that transforms one image into another. This framework encompasses a broad range of transformations including stereo, optical flow, lighting changes, blur, and combinations of these effects. Parametric models such as affine motion, vignetting, and radial distortion can also be modeled within the same framework. All such transformations are modeled by selecting a number of constraints and objectives on the filter entries from a catalog which we enumerate. Most of the constraints are linear, leading to globally optimal solutions (via linear programming) for affine transformations, depth-from-defocus, and other problems. Adding a (non-convex) compactness objective enables solutions for optical flow with illumination changes, spacevariant defocus, and higher-order smoothness. 1
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