Schirrmacher F, Riess C, Köhler T (2020)
Publication Type: Journal article
Publication year: 2020
Book Volume: 6
Pages Range: 503--517
Journal Issue: 1
URI: https://faui1-files.cs.fau.de/public/publications/mmsec/2020-Schirrmacher-AQS.pdf
Inverse problems play a central role for many classical computer vision and image processing tasks. Many inverse problems are ill-posed, and hence require a prior to regularize the solution space. However, many of the existing priors, like total variation, are based on ad-hoc assumptions that have difficulties to represent the actual distribution of natural images. Thus, a key challenge in research on image processing is to find better suited priors to represent natural images. In this article, we propose the Adaptive Quantile Sparse Image (AQuaSI) prior. It is based on a quantile filter, can be used as a joint filter on guidance data, and be readily plugged into a wide range of numerical optimization algorithms. We demonstrate the efficacy of the proposed prior in joint RGB/depth upsampling, on RGB/NIR image restoration, and in a comparison with related regularization by denoising approaches.
APA:
Schirrmacher, F., Riess, C., & Köhler, T. (2020). Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems. IEEE Transactions on Computational Imaging, 6(1), 503--517. https://dx.doi.org/10.1109/TCI.2019.2956888
MLA:
Schirrmacher, Franziska, Christian Riess, and Thomas Köhler. "Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems." IEEE Transactions on Computational Imaging 6.1 (2020): 503--517.
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