Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems

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 d...

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Bibliographic Details
Published in:IEEE transactions on computational imaging 2020, Vol.6, p.503-517
Main Authors: Schirrmacher, Franziska, Riess, Christian, Kohler, Thomas
Format: Article
Language:English
Subjects:
Algorithms
Computer vision
Ill posed problems
Image processing
Image restoration
Imaging
Inverse problems
Noise reduction
Optimization
Regularization
Solution space
Task analysis
universal image prior
weighted quantile filter
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Summary: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.
ISSN:2573-0436
2333-9403
DOI:10.1109/TCI.2019.2956888