Abstract
This paper proposes an optimization-based blind image deconvolution method. The proposed method relies on imposing a discrete MRF prior on the deconvolved image. The use of such a prior leads to a very efficient and powerful deconvolution algorithm that carefully combines advanced optimization techniques. We demonstrate the extreme effectiveness of our method by applying it on a wide variety of very challenging cases that involve the inference of large and complicated blur kernels.
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Komodakis, N., Paragios, N. (2013). MRF-Based Blind Image Deconvolution. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds) Computer Vision – ACCV 2012. ACCV 2012. Lecture Notes in Computer Science, vol 7726. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37431-9_28
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DOI: https://doi.org/10.1007/978-3-642-37431-9_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37430-2
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