There are three types of blur in images: defocus, camera shake, and object motion. While defocus and camera shake have received considerable attention, object motion is by far the most difficult feature to detect due to its nonstationarity. Object motion blur, however, is potentially useful for scene analysis because it provides temporal cues from a single image. We developed a technique to infer object motion by detecting spatially varying motion blur from a single image. We are able to remove the blur without introducing ringing (an artifact that all conventional methods suffer from) or iterative steps (hence faster than most deconvolution methods).