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We consider a problem of reconstructing an image from incomplete quadratic measurements by minimizing its total variation. The problem of reconstructing an object from incomplete nonlinear acquisitions arises in many applications, such as astronomical imaging or depth retrieval. Placing ourselves in a discrete setting, we provide theoretical guarantees for stable and robust image recovery from incomplete noisy quadratic measurements.
This is poster number 31 in Poster Session