Date: Monday, February 11 2008
Speaker: L.J. van Vliet, TU Delft
Room and time: 5161.0267 (Bernoulliborg), 16.00
Algorithms and Performance Measures.
Super-resolution restoration addresses the problem of restoring a high-resolution scene from multiple degraded low-resolution images under motion. Due to imaging blur and noise, this problem is ill-posed. We propose two strategies to tackle this problem. The first employs a robust fusion step based on structure-adaptive normalized convolution. The second solves an inverse problem without explicit additional smoothness constraints to obtain a stable solution. While adding a regularization term to the cost function is a standard practice in image restoration, we propose a restoration algorithm that does not require this extra regularization term. The robustness of the algorithm is achieved by a Gaussian-weighted L2-norm in the data misfit term that effectively ignores intensity outliers. With the outliers suppressed, our solution behaves similarly to a maximum-likelihood solution in the
presence of Gaussian noise. The effectiveness of our algorithm is demonstrated with super-resolution restoration of real infrared image sequences under severe aliasing and intensity outliers.
Colloquiumcoördinator is Prof.dr. M. Aiello, Dr. M. Biehl (e-mail: M.Biehl@cs.rug.nl)