Blur kernel estimation using the Radon Transform

Camera shake is a common source of degradation in photographs. Restoring blurred pictures is challenging because both the blur kernel and the sharp image are unknown, which makes this problem severely underconstrained. In this work, we estimate camera shake by analyzing edges in the image, effectively constructing the Radon transform of the kernel. Building upon this result, we describe two algorithms for estimating spatially invariant blur kernels. In the first method, we directly invert the transform, which is computationally efficient since it is not necessary to also estimate the latent sharp image. This approach is well suited for scenes with a diversity of edges, such as man-made environments. In the second method, we incorporate the Radon transform within the MAP estimation framework to jointly estimate the kernel and the image. While more expensive, this algorithm performs well on a broader variety of scenes, even when fewer edges can be observed. Our experiments show that o...

• Type: Journal
• Year: 2011
• Where: CVPR
• Authors: Taeg Sang Cho, Sylvain Paris, Bill Freeman, Berthold Horn
• Keywords: Blur Kernels, Computer Vision, CVPR 2011, Radon Transform, Sharp Image