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IMAGE INPAINTING FROM PARTIAL NOISY DATA BY DIRECTIONAL COMPLEX TIGHT FRAMELETS

Part of: Communication, information Nontrigonometric harmonic analysis Numerical methods in Fourier analysis

Published online by Cambridge University Press:  26 May 2017

YI SHEN Open the ORCID record for YI SHEN [Opens in a new window] ,
BIN HAN  and
ELENA BRAVERMAN Open the ORCID record for ELENA BRAVERMAN [Opens in a new window]
YI SHEN*
Affiliation:
Department of Mathematics, Zhejiang Sci-Tech University, Hangzhou 310028, China email [email protected] Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta T6G 2G1, Canada email [email protected] Department of Mathematics and Statistics, University of Calgary, 2500 University Drive N.W., Calgary, Alberta T2N 1N4, Canada email [email protected]
BIN HAN
Affiliation:
Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta T6G 2G1, Canada email [email protected]
ELENA BRAVERMAN
Affiliation:
Department of Mathematics and Statistics, University of Calgary, 2500 University Drive N.W., Calgary, Alberta T2N 1N4, Canada email [email protected]
*
✉[email protected]
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Abstract

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Image inpainting methods recover true images from partial noisy observations. Natural images usually have two layers consisting of cartoons and textures. Methods using simultaneous cartoon and texture inpainting are popular in the literature by using two combined tight frames: one (often built from wavelets, curvelets or shearlets) provides sparse representations for cartoons and the other (often built from discrete cosine transforms) offers sparse approximation for textures. Inspired by the recent development on directional tensor product complex tight framelets ($\text{TP}\text{-}\mathbb{C}\text{TF}$s) and their impressive performance for the image denoising problem, we propose an iterative thresholding algorithm using tight frames derived from $\text{TP}\text{-}\mathbb{C}\text{TF}$s for the image inpainting problem. The tight frame $\text{TP}\text{-}\mathbb{C}\text{TF}_{6}$ contains two classes of framelets; one is good for cartoons and the other is good for textures. Therefore, it can handle both the cartoons and the textures well. For the image inpainting problem with additive zero-mean independent and identically distributed Gaussian noise, our proposed algorithm does not require us to tune parameters manually for reasonably good performance. Experimental results show that our proposed algorithm performs comparatively better than several well-known frame systems for the image inpainting problem.

Keywords

noisy dataimage inpaintingdirectional tensor product complex tight frameletssparse representationiterative scheme

MSC classification

Primary: 42C40: Wavelets and other special systems
Secondary: 42C15: General harmonic expansions, frames 65T60: Wavelets 94A08: Image processing (compression, reconstruction, etc.)
Type
Research Article
Information
The ANZIAM Journal , Volume 58 , Issue 3-4 , April 2017 , pp. 247 - 255
Copyright
© 2017 Australian Mathematical Society 

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