Kernel Diffusion: An Alternate Approach to Blind Deconvolution

Understand

Blind deconvolution problems are severely ill-posed because neither the underlying signal nor the forward operator are not known exactly.

• Conventionally, these problems are solved by alternating between estimation of the image and kernel while keeping the other fixed.
• In this paper, we show that this framework is flawed because of its tendency to get trapped in local minima and, instead, suggest the use of a kernel estimation strategy with a non-blind solver.
• This framework is employed by a diffusion method which is trained to sample the blur kernel from the conditional distribution with guidance from a pre-trained non-blind solver.