An Incremental GEM Framework for Multiframe Blind Deconvolution, Super-Resolution, and Saturation Correction

We develop an incremental generalized expectation maximization (GEM) framework to model the multiframe blind deconvolution problem. A simplistic version of this problem was recently studied by Harmeling et al. [4]. We solve a more realistic version of this problem which includes the following major features: (i) super-resolution ability despite noise and unknown blurring; (ii) saturationcorrection, i.e., handling of overexposed pixels that can otherwise confound the image processing; and (iii) simultaneous handling of color channels. These features are seamlessly integrated into our incremental GEM framework to yield simple but efficient multiframe blind deconvolution algorithms. We present technical details concerning critical steps of our algorithms, especially to highlight how all operations can be written using matrix-vector multiplications. We apply our algorithm to real-world images from astronomy and super resolution tasks. Our experimental results show that our methods yield improved resolution and deconvolution at the same time.