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Recent results on Bayesian image recovery

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If you have a question about this talk, please contact Rachel Fogg.

There is a plethora of image and video processing applications in which information is lost due to acquisition, processing, or transmission. These applications include astronomical imaging, commercial and entertainment photography, medical imaging, and molecular and cellular bio-imaging. A recovery problem is then required to be solved, according to which an estimate of the original scene is obtained based on the observed data and prior knowledge about the original image and the degradation process. Examples of such problems include image and video restoration, super-resolution, the removal of compression and transmission artifacts, compressive sensing and light field estimation in computational photography. We describe in detail some of our recent results and outstanding challenges in image recovery utilizing a Bayesian framework. Experimental results are presented to illustrate and compare the effectiveness of various approaches.

This talk is part of the Signal Processing and Communications Lab Seminars series.

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