Edge-based image restoration

Rares, A.; Reinders, Marcel J. T.; Biemond, J.

doi:10.1109/tip.2005.854466

Cited by 71 publications

(59 citation statements)

References 26 publications

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“…Since multiple estimation results are obtained for each pixel, we output the optimal results that minimize the criterion shown in Eq. (14). Furthermore, in our method, K = 8.…”

Section: B Results Of Super-resolutionmentioning

confidence: 99%

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Adaptive Subspace-Based Inverse Projections via Division Into Multiple Sub-Problems for Missing Image Data Restoration

Ogawa

Haseyama

2016

IEEE Trans. on Image Process.

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Abstract-This paper presents adaptive subspace-based inverse projections via division into multiple sub-problems (ASIP-DIMS) for missing image data restoration. In the proposed method, a target problem for estimating missing image data is divided into multiple sub-problems, and each sub-problem is iteratively solved with constraints of other known image data. By projection into a subspace model of image patches, the solution of each subproblem is calculated, where we call this procedure "subspacebased inverse projection" for simplicity. The proposed method can use higher-dimensional subspaces for finding unique solutions in each sub-problem, and successful restoration becomes feasible since a high level of image representation performance can be preserved. This is the biggest contribution of this paper. Furthermore, the proposed method generates several subspaces from known training examples and enables derivation of a new criterion in the above framework to adaptively select the optimal subspace for each target patch. In this way, the proposed method realizes missing image data restoration using ASIP-DIMS. Since our method can estimate any kind of missing image data, its potential in two image restoration tasks, image inpainting and super-resolution, based on several methods for multivariate analysis is also shown in this paper.

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“…Since multiple estimation results are obtained for each pixel, we output the optimal results that minimize the criterion shown in Eq. (14). Furthermore, in our method, K = 8.…”

Section: B Results Of Super-resolutionmentioning

confidence: 99%

“…In the explanations of the previous section, the most important points of ASIP-DIMS are Eqs. (1), (12), (13) and (14). Therefore, in each subsection, we show how these equations are derived for each method of multivariate analysis.…”

Section: Specific Derivations Of Asip-dimsmentioning

confidence: 99%

Adaptive Subspace-Based Inverse Projections via Division Into Multiple Sub-Problems for Missing Image Data Restoration

Ogawa

Haseyama

2016

IEEE Trans. on Image Process.

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“…Through the use of structural approximate curve first complete structure repair, dividing the whole image for repair again, and this algorithm has a great influence on the solution of the structural deviation. That same year, Andrei and Marcel put forward the repair method based on the edge through calculating the edge of the minimum cost to reconstruct the image [8] . Shen and others by using structure information to boot image restoration, in a certain extent, improved the structure after the restoration of blurring effect, and at the same time improve the efficiency of texture synthesis [9] .…”

Section: The Digital Image Restoration Based On the Structurementioning

confidence: 99%

A Survey on Thangka Image Inpainting Method Based on Structure-borne

Liu¹

2015

Proceedings of the 4th International Conference on Information Technology and Management Innovation

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Abstract. The ancient Tangka image with reflecting Tibet art has been broken by the nature and human with different degrees．The part-shed Tangka image is researched as a type of special breaking. After discussing digital image inpainting, the digital image restoration based on the structure, and Thangka image inpainting method, we give some direction of the development of Tangka image restoration based on structure-borne.

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“…Attractive methods that perform simultaneous reconstruction of missing structures and textures in images have also been proposed [19]- [13]. Most of the algorithms so far reported are based on structural inpainting techniques for accurate reconstruction of missing edges [5]- [7]. These techniques are effective for pure structure images.…”

Section: Introductionmentioning

confidence: 99%

Missing Image Data Reconstruction Based on Adaptive Inverse Projection via Sparse Representation

Ogawa

Haseyama

2011

IEEE Trans. Multimedia

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Abstract-In this paper, a missing image data reconstruction method based on an adaptive inverse projection via sparse representation is proposed. The proposed method utilizes sparse representation for obtaining low-dimensional subspaces that approximate target textures including missing areas. Then, by using the obtained low-dimensional subspaces, inverse projection for reconstructing missing areas can be derived to solve the problem of not being able to directly estimate missing intensities. Furthermore, in this approach, the proposed method monitors errors caused by the derived inverse projection, and the lowdimensional subspaces optimal for target textures are adaptively selected. Therefore, we can apply adaptive inverse projection via sparse representation to target missing textures, i.e., their adaptive reconstruction becomes feasible. The proposed method also introduces some schemes for color processing into the calculation of subspaces on the basis of sparse representation and attempts to avoid spurious color caused in the reconstruction results. Consequently, successful reconstruction of missing areas by the proposed method can be expected. Experimental results show impressive improvement of our reconstruction method over previously reported reconstruction methods.

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Edge-based image restoration

Cited by 71 publications

References 26 publications

Adaptive Subspace-Based Inverse Projections via Division Into Multiple Sub-Problems for Missing Image Data Restoration

Adaptive Subspace-Based Inverse Projections via Division Into Multiple Sub-Problems for Missing Image Data Restoration

A Survey on Thangka Image Inpainting Method Based on Structure-borne

Missing Image Data Reconstruction Based on Adaptive Inverse Projection via Sparse Representation

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