Inpainting via sparse recovery with directional constraints

Xuemei Chen; Julia Dobrosotskaya

doi:10.3934/mfc.2020025

Article Contents

2020, Volume 3, Issue 4: 229-247. Doi: 10.3934/mfc.2020025

This issue Previous Article Sketch-based image retrieval via CAT loss with elastic net regularization Next Article Network centralities, demographic disparities, and voluntary participation

Inpainting via sparse recovery with directional constraints

Xuemei Chen^1, , and
Julia Dobrosotskaya^2,

1.
Department of Mathematics and Statistics, University of North Carolina Wilmington, Wilmington, NC 28403, USA
2.
Department of Mathematics, Applied Mathematics and Statistics, Case Western Reserve University, Cleveland, OH 44106, USA

^* Corresponding author: Xuemei Chen
^* Corresponding author: Xuemei Chen

Received: January 31, 2020

Revised: September 30, 2020

Early access: November 2020

Published: November 2020

The first author is supported by NSF DMS-2050028

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Abstract

Image inpainting is a particular case of image completion problem. We describe a novel method allowing to amend the general scenario of using sparse or TV-based recovery for inpainting purposes by an efficient use of adaptive one-dimensional directional "sensing" into the unknown domain. We analyze the smoothness of the image near each pixel on the boundary of the unknown domain and formulate linear constraints designed to promote smooth transitions from the known domain in the directions where smooth behavior have been detected. We include a theoretical result relaxing the widely known sufficient condition of sparse recovery based on coherence, as well as observations on how adding the directional constraints can improve the well-posedness of sparse inpainting.

The numerical implementation of our method is based on ADMM. Examples of inpainting of natural images and binary images with edges crossing the unknown domain demonstrate significant improvement of recovery quality in the presence of adaptive directional constraints. We conclude that the introduced framework is general enough to offer a lot of flexibility and be successfully utilized in a multitude of image recovery scenarios.

Keywords:

Mathematics Subject Classification: Primary: 65F45, 65K10; Secondary: 94A16.

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Figure 1. An image to be inpainted

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Figure 2. Forming boundary equations

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Figure 3. Forming boundary equations: pick the best direction

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Figure 4. Recovery via directional sensing only. (a) Image with a thin missing domain. (b) 'DB-2', all smooth directions per pixel included, both centered and shifted equations used. (c) 'DB-2', all smooth directions per pixel included, only centered equations used. (d) 'DB-2', one direction per pixel included, only centered equations used could be formed as the filter has length 2. (e) 'DB-4', all smooth directions included, both centered and shifted equations used

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Figure 5. Examples of directional constraints adaptively formulated for the inpainting examples discussed further in the text. In both cases the 'DB2' filter of length 4 was used to form centered equations. Green dots indicate the unknown pixels around which the constraints were formed. Red dots indicate other pixels present in those constraints. The constraints are shown only for some pixels to avoid overcrowding the images. The actual results of inpainting appear later in Figures 6 and 9

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Figure 9. Text removal of Pepper, $ 128\times128 $

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Figure 6. Inpaint a missing block of a vertical stripe, $ 64\times64 $

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Figure 7. Inpainting a thin missing block in an image with repetitive pattern of slanted stripes, $ 64\times64 $

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Figure 8. Inpaint a missing annulus of a sectional image, $ 128\times128 $

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Figure 10. Smooth stripes inpainting, $ 64\times64 $

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Figure 11. Text removal of a colored image, $ 128\times128 $

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Table 1. Here $ \sqrt{\beta} = .75 $, $ F $ is the reshaped 2D DB-4 wavelet basis matrix, the first filter $ h $ is a high pass DB-2 filter, all smooth directions used and only centered equations included, the second filter added is a high pass DB-4 filter

	Cosine	Coherence $ \mu_1 $	Min singular value	Max singular value
$ P_{\Lambda} F $	0.8541	3.8339	0	1
$ SF $ (1 filter)	0.4799	0.5888	0.0455	1.5255
$ S_2F $ (2 filters)	0.3913	0.4480	0.0937	1.9837

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Table 2. Here $ \sqrt{\beta} = .75 $, $ F $ is the reshaped 2D binary Weyl basis matrix, the filter $ h $ is a high pass DB-2 filter, all smooth directions used and only centered equations included

	Cosine	Coherence $ \mu_1 $	Min singular value	Max singular value
$ P_{\Lambda} F $	0.0667	0.0667	0	1
$ SF $ (1 filter)	0.0452	0.0457	0.0455	1.5255

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