Patch-Based Image Inpainting via Two-Stage Low Rank Approximation

Guo, Qiang; Gao, Shanshan; Zhang, Xiaofeng; Yin, Yilong; Zhang, Caiming

doi:10.1109/tvcg.2017.2702738

Cited by 128 publications

(67 citation statements)

References 54 publications

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“…Image inpainting was originally a traditional graphics problem, mainly based on mathematical and physical methods, using the existing information in the image to restore the defective part of the image. For the defective area in the image, starting from the edge of the target area, using the structure of the non-target area and texture information, the unknown area is predicted and patched according to the matching criteria, so that the filled image is visually reasonable and real [6]. According to different principles, digital image inpainting algorithms can be divided into two categories: structural propagation methods based on partial differential equations (PDEs) [7] and texture synthesis methods based on sample block [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Face Image Inpainting Algorithm Based on Feature Symmetry

Niu

Yao

et al. 2020

Symmetry

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Face image inpainting technology is an important research direction in image restoration. When the current image restoration methods repair the damaged areas of face images with weak texture, there are problems such as low accuracy of face image decomposition, unreasonable restoration structure, and degradation of image quality after inpainting. Therefore, this paper proposes an adaptive face image inpainting algorithm based on feature symmetry. Firstly, we locate the feature points of the face, and segment the face into four feature parts based on the feature point distribution to define the feature search range. Then, we construct a new mathematical model, introduce feature symmetry to improve priority calculation, and increase the reliability of priority calculation. After that, in the process of searching for matching blocks, we accurately locate similar feature blocks according to the relative position and symmetry criteria of the target block and various feature parts of the face. Finally, we introduced the HSV (Hue, Saturation, Value) color space to determine the best matching block according to the chroma and brightness of the sample, reduce the repair error, and complete the face image inpainting. During the experiment, we firstly performed visual evaluation and texture analysis on the inpainting face image, and the results show that the face image inpainting by our algorithm maintained the consistency of the face structure, and the visual observation was closer to the real face features. Then, we used the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as objective evaluation indicators; among the five sample face images inpainting results given in this paper, our method was better than the reference methods, and the average PSNR value improved from 2.881–5.776 dB using our method when inpainting 100 face images. Additionally, we used the time required for inpainting the unit pixel to evaluate the inpainting efficiency, and it was improved by 12%–49% with our method when inpainting 100 face images. Finally, by comparing the face image inpainting experiments with the generative adversary network (GAN) algorithm, we discuss some of the problems with the method in this paper based on graphics in repairing face images with large areas of missing features.

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Section: Introductionmentioning

confidence: 99%

“…The confidence of the pixels in the repaired area is mainly updated, and then the next pixel is prepared for inpainting. Steps (2)(3)(4)(5)(6)(7)(8) are repeated until the face image is repaired. 9.…”

mentioning

confidence: 99%

An Adaptive Face Image Inpainting Algorithm Based on Feature Symmetry

Niu

Yao

et al. 2020

Symmetry

View full text Add to dashboard Cite

show abstract

“…As a fundamental ill-posed inverse problem in image processing and low-level vision, digital image restoration aims to reconstruct a latent high-quality image from its degraded observation [7,11]. Up to now, image restoration technology has made great progress, and many advanced methods have been introduced based on a variety of optimization models, mainly including variational calculus and partial differential equations [4,5,8,12], methods based on such priors as exemplar matching and synthesis [13][14][15], sparse representation and low rank approximation [16][17][18][19][20][21][22][23][24][25][26], etc.…”

Section: Introductionmentioning

confidence: 99%

“…For example, an efficient filtering algorithm is proposed in [20] to sparsely represent image patches using singular value decomposition (SVD) and to remove noise in image by iterative singular value shrinkage. Another algorithm proposed in [26] utilizes nonlocal selfsimilarity (NSS) and low rank approximation, which includes two steps of SVD based on a special hard thresholding.…”

Section: Introductionmentioning

confidence: 99%

Image Restoration Based on Gradual Reweighted Regularization and Low Rank prior

Wang

2020

Mathematical Problems in Engineering

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Digital restoration of image with missing data is a basic need for visual communication and industrial applications. In this paper, making full use of priors of low rank and nonlocal self-similarity a gradual reweighted regularization is proposed for matrix completion and image restoration. Sparsity-promoting regularization produces much sparser representation of grouped nonlocal similar blocks of image by solving a nonconvex minimization problem. Moreover, an alternation direction method of multipliers algorithm is developed to speed up iterative solving of the above problem. Image block classification further enhances the adaptivity of the proposed method. Experiments on simulated matrix and natural image show that the proposed method obtains better image restoration results, where most lost information is reorcovered and few artifacts are produced.

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“…A simulated annealing algorithm is used to solve this metric labeling problem to generate images with better visual quality. In paper [12] a method for image inpainting using low rank approximation, which avoids the time-consuming iterative shrinkage is proposed. The similar patches of a corrupted image are identified and reshaped as vectors, then a patch matrix can be constructed by collecting these similar patchvectors.…”

Section: Introductionmentioning

confidence: 99%

Inpainting of Structural Reconstruction of Monuments Using Singular Value Decomposition Refinement of Patches

Awati¹,

Patil²

2019

IJIGSP

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Image Inpainting of ruined historic monuments and heritage sites can help in visualizing how these may have existed in the past. An inpainted image of a monument can serve as a tool for physical reconstruction purpose. The purpose of the proposed method is to fill cracks and gaps of selected damaged regions in heritage monuments by exploiting the statistical properties of foreground and background along with the spatial location of the damage in the image of the monuments. The patch based image inpainting algorithm is improved by segmenting the image using K means clustering to search the candidate patches in relevant source region only. Segmentation improves patch searching in terms of both quality and time. The priority of the patch to fill is decided based on the standard deviation of the patch around destination pixel. K n similar patches are selected from the source region based on minimum value of sum squared distance. The selected patches are refined using an efficient patch refinement scheme using higher order singular value decomposition to capture underlying pattern among the candidate source patches. The threshold for refinement is selected by using minimum and maximum value of standard deviation of the target patch. This eliminates random variation and unwanted artifacts. Experimental results carried on a large number of natural images and comparisons with well-known existing methods demonstrate the efficacy and superiority of the proposed method.

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Patch-Based Image Inpainting via Two-Stage Low Rank Approximation

Cited by 128 publications

References 54 publications

An Adaptive Face Image Inpainting Algorithm Based on Feature Symmetry

An Adaptive Face Image Inpainting Algorithm Based on Feature Symmetry

Image Restoration Based on Gradual Reweighted Regularization and Low Rank prior

Inpainting of Structural Reconstruction of Monuments Using Singular Value Decomposition Refinement of Patches

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