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arXiv · 1401.4107

Revisiting loss-specific training of filter-based MRFs for image restoration

Yunjin Chen·Thomas Pock·René Ranftl·Horst Bischof
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Abstract

It is now well known that Markov random fields (MRFs) are particularly effective for modeling image priors in low-level vision. Recent years have seen the emergence of two main approaches for learning the parameters in MRFs: (1) probabilistic learning using sampling-based algorithms and (2) loss-specific training based on MAP estimate. After investigating existing training approaches, it turns out that the performance of the loss-specific training has been significantly underestimated in existing work. In this paper, we revisit this approach and use techniques from bi-level optimization to solve it. We show that we can get a substantial gain in the final performance by solving the lower-level problem in the bi-level framework with high accuracy using our newly proposed algorithm. As a result, our trained model is on par with highly specialized image denoising algorithms and clearly outperforms probabilistically trained MRF models. Our findings suggest that for the loss-specific training scheme, solving the lower-level problem with higher accuracy is beneficial. Our trained model comes along with the additional advantage, that inference is extremely efficient. Our GPU-based implementation takes less than 1s to produce state-of-the-art performance.

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Yunjin Chen, Thomas Pock, René Ranftl, Horst Bischof. 2014-01-16. Revisiting loss-specific training of filter-based MRFs for image restoration. https://doi.org/10.1007/978-3-642-40602-7_30

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