Modeling and removing spatially-varying optical blur

Photo deblurring has been a major research topic in the past few years. So far, existing methods have focused on removing the blur due to camera shake and object motion. In this paper, we show that the optical system of the camera also generates significant blur, even with professional lenses. We introduce a method to estimate the blur kernel densely over the image and across multiple aperture and zoom settings. Our measures show that the blur kernel can have a non-negligible spread, even with top-of-the-line equipment, and that it varies nontrivially over this domain. In particular, the spatial variations are not radially symmetric and not even left-right symmetric. We develop and compare two models of the optical blur, each of them having its own advantages. We show that our models predict accurate blur kernels that can be used to restore photos. We demonstrate that we can produce images that are more uniformly sharp unlike those produced with spatially-invariant deblurring techniques.