Blind Deconvolution in Image Processing

I will address the problem of estimating a convolution kernel from degraded measurements and removal of the degradation using state-of-the-artalgorithms. This blind deconvolution problem is ill-posed and conclusions will be drawn that additional prior information is absolutely essential for reaching stability. I then provide details on different strategies that secure the aforementioned prior information targeting primarily applications in image processing. In many practical situations the convolution kernel is space-variant, which renders the problem even more challenging. I will briefly survey approximation techniques of the space-variant degradation operator and show results on real data.