Abstract
In our recent work, the Markov chain Monte Carlo (MCMC) technique has been successfully exploited and shown as an effective approach to perform super-resolution image reconstruction. However, one major challenge lies at the selection of the hyperparameter of the prior image model, which affects the degree of regularity imposed by the prior image model, and consequently, the quality of the estimated high-resolution image. To tackle this challenge, in this paper, we propose a novel approach to automatically adapt the model’s hyperparameter during the MCMC process, rather than the exhaustive, off-line search. Experimental results presented show that the proposed hyperparameter adaptation method yields extremely close performance to that of the optimal prior image model case.
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Tian, J., Ma, KK. (2006). Markov Chain Monte Carlo Super-Resolution Image Reconstruction with Simultaneous Adaptation of the Prior Image Model. In: Zhuang, Y., Yang, SQ., Rui, Y., He, Q. (eds) Advances in Multimedia Information Processing - PCM 2006. PCM 2006. Lecture Notes in Computer Science, vol 4261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11922162_34
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DOI: https://doi.org/10.1007/11922162_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-48766-1
Online ISBN: 978-3-540-48769-2
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