OverviewImage blur is difficult to avoid in many situations and can often ruin a photograph. Deblurring an image is an inherently ill-posed problem. The observed blurred image only provides a partial constraint on the solution ï¿½ with no additional constraints, there are infinitely many blur kernels and images that can be convolved together to match the observed blurred image. Even if the blur kernel is known, there still could be many ï¿½sharpï¿½ images that when convolved with the blur kernel can match the observed blurred and noisy image. One of the central challenge in image deblurring is to develop methods that can disambiguate between potential multiple solutions and bias a deblurring processes toward more likely results given some prior information. We are investigating new image priors that are more constraining that those that are typically used. We are investigating both PSF/blur kernel estimation and non-blind deconvolution. Our work in this area has resulted in methods to create sharper, higher-quality images from a blurry input images.