• Title, Summary, Keyword: image inpainting

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A Study on The performance Improvement of Image inpainting (Image inpainting의 성능 개선에 관한 연구)

  • Gong, Jae-Woong;Kim, Sung-Hyun;Kim, Tae-Hyoung;Kim, Doo-Yung
    • Proceedings of the Korea Institute of Convergence Signal Processing
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    • pp.221-224
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    • 2005
  • 대부분의 영상은 다양한 이유(노이즈, 전송과정 중 발생하는 문제 등)로 인해 항상 좋은 품질을 보여주진 못한다. 이렇게 훼손된 영상의 복원은 다양한 정보를 제공한다. 이런 훼손된 영상을 복원하기 위해 Median filtering과 같은 기존의 처리 방법들은 주변 화소(Pixel)를 평활화(Smoothing) 처리를 하기 때문에 noise 처리에는 좋으나 원 영상의 중요한 에지 성분까지도 평활화 처리를 함으로써 에지 부분의 공간적 이동을 초래할 수 있다. 이러한 문제점을 해결하기 위하여 image inpainting 방법이 제안되고 있으며, inpainting 기법에는 편미분 방정식(PDE)을 이용한 방법, 텍스쳐 병합 기반의 방법들이 있다. 그러나 이러한 inpainting 기법들은 연산 수행시간이 많이 소요되는 단점이 있다. 따라서 본 연구에서는 image inpainting을 수행시 소요되는 연산시간을 줄이는 fast image inpainting 알고리즘을 제안한다.

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Thangka Image Inpainting Algorithm Based on Wavelet Transform and Structural Constraints

  • Yao, Fan
    • Journal of Information Processing Systems
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    • v.16 no.5
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    • pp.1129-1144
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    • 2020
  • The thangka image inpainting method based on wavelet transform is not ideal for contour curves when the high frequency information is repaired. In order to solve the problem, a new image inpainting algorithm is proposed based on edge structural constraints and wavelet transform coefficients. Firstly, a damaged thangka image is decomposed into low frequency subgraphs and high frequency subgraphs with different resolutions using wavelet transform. Then, the improved fast marching method is used to repair the low frequency subgraphs which represent structural information of the image. At the same time, for the high frequency subgraphs which represent textural information of the image, the extracted and repaired edge contour information is used to constrain structure inpainting in the proposed algorithm. Finally, the texture part is repaired using texture synthesis based on the wavelet coefficient characteristic of each subgraph. In this paper, the improved method is compared with the existing three methods. It is found that the improved method is superior to them in inpainting accuracy, especially in the case of contour curve. The experimental results show that the hierarchical method combined with structural constraints has a good effect on the edge damage of thangka images.

Texture Garbage Elimination Algorithm for Exemplar-based Image Inpainting (예제기반 영상 인페인팅을 위한 텍스쳐 가비지 제거 알고리즘)

  • Kong, Young Il;Lee, Si-Woong
    • Journal of Broadcast Engineering
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    • v.24 no.1
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    • pp.186-189
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    • 2019
  • Image inpainting is an image processing technique that restores an image by naturally filling the empty or damaged regions in an image. In this paper, we present a new image inpainting technique that can suppress the generation of texture garbage which is one of the artifacts of existing exemplar-based image inpainting. Unlike the existing technique, only the stationary source patch is sampled as the exemplar patch based on the assumption of spatial stationarity of the texture. This prevents the texture garbage, which is an inconsistent piece of texture from being copied to the target region. Experimental results show that the texture synthesis using the proposed method produces more natural inpainting results than the existing method.

A Design of Block-wise Inpainting Scheme for Packet Error Concealment (패킷에러로 인한 영상손실을 최소화하기 위한 블록기반의 인페인팅 알고리즘의 설계)

  • Feng, Liu;Han, Ngoc Son;Kim, Seong Whan
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.349-350
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    • 2009
  • In this paper, we describe an error concealment techniques based on image inpainting for the image impairments due to the packet loss. Image inpainting is to remove or restore the damaged sections from the images, which is usually old images, paintings, or video films. Inpainting has a long history which goes back to the era when the paintings come out. Manual inpainting is no more used, and we can use digital inpainting for the digitally impaired images and video sequences. In this paper, we review the error concealment techniques for the packet loss recovery and propose our inpainting based image impairment recovery scheme for video communication over packet networks.

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Neighboring Elemental Image Exemplar Based Inpainting for Computational Integral Imaging Reconstruction with Partial Occlusion

  • Ko, Bumseok;Lee, Byung-Gook;Lee, Sukho
    • Journal of the Optical Society of Korea
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    • v.19 no.4
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    • pp.390-396
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    • 2015
  • We propose a partial occlusion removal method for computational integral imaging reconstruction (CIIR) based on the usage of the exemplar based inpainting technique. The proposed method is an improved version of the original linear inpainting based CIIR (LI-CIIR), which uses the inpainting technique to fill in the data missing region. The LI-CIIR shows good results for images which contain objects with smooth surfaces. However, if the object has a textured surface, the result of the LI-CIIR deteriorates, since the linear inpainting cannot recover the textured data in the data missing region well. In this work, we utilize the exemplar based inpainting to fill in the textured data in the data missing region. We call the proposed method the neighboring elemental image exemplar based inpainting (NEI-exemplar inpainting) method, since it uses sources from neighboring elemental images to fill in the data missing region. Furthermore, we also propose an automatic occluding region extraction method based on the use of the mutual constraint using depth estimation (MC-DE) and the level set based bimodal segmentation. Experimental results show the validity of the proposed system.

Exemplar-based Image Inpainting Using Multiple Patches (다중 패치를 이용한 예제 기반 영상 인페인팅)

  • Park, Chan-Woo;Lee, San-Hyun;Park, Ki-Tae;Moon, Young-Shik
    • Journal of the Institute of Electronics Engineers of Korea CI
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    • v.48 no.1
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    • pp.8-16
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    • 2011
  • Image inpainting is a technique for removing damaged regions and reconstructing them with visually plausible backgrounds. However, if size of the damaged regions for reconstructing is large, the unexpected results can be obtained due to disconnected structures within reconstructed regions. In this paper, by considering spatial distance information between candidate patches and a damaged patch as well as pixel value difference, an exemplar-based image inpainting using multiple patches is proposed. In conventional exemplar-based image inpainting method, implausible results such as blocking effects or repetition of reconstructed patch may occur by using inappropriately selected single patch. To improve the exemplar-based method, the weighted sum of multiple patches considering both the spatial distance and the pixel value difference between the target patch and the candidate patches is utilized. Experimental results have shown that the proposed method produces better performance of image inpainting than the existing method.

Image Reconstruction of Sinogram Restoration using Inpainting method in Sparse View CT (Sparse view CT에서 inpainting 방법을 이용한 사이노그램 복원의 영상 재구성)

  • Kim, Daehong;Baek, Cheol-Ha
    • Journal of the Korean Society of Radiology
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    • v.11 no.7
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    • pp.655-661
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    • 2017
  • Sparse view CT has been widely used to reduce radiation dose to patient in radiation therapy. In this work, we performed sinogram restoration from sparse sampling data by using inpainting method for simulation and experiment. Sinogram restoration was performed in accordance with sampling angle and restoration method, and their results were validated with root mean square error (RMSE) and image profiles. Simulation and experiment are designed to fan beam scan for various projection angles. Sparse data in sinogram were restored by using linear interpolation and inpainting method. Then, the restored sinogram was reconstructed with filtered backprojection (FBP) algorithm. The results showed that RMSE and image profiles were depended on the projection angles and restoration method. Based on the simulation and experiment, we found that inpainting method could be improved for sinogram restoration in comparison to linear interpolation method for estimating RMSE and image profiles.

Image Restoration and Object Removal Using Prioritized Adaptive Patch-Based Inpainting in a Wavelet Domain

  • Borole, Rajesh P.;Bonde, Sanjiv V.
    • Journal of Information Processing Systems
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    • v.13 no.5
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    • pp.1183-1202
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    • 2017
  • Image restoration has been carried out by texture synthesis mostly for large regions and inpainting algorithms for small cracks in images. In this paper, we propose a new approach that allows for the simultaneous fill-in of different structures and textures by processing in a wavelet domain. A combination of structure inpainting and patch-based texture synthesis is carried out, which is known as patch-based inpainting, for filling and updating the target region. The wavelet transform is used for its very good multiresolution capabilities. The proposed algorithm uses the wavelet domain subbands to resolve the structure and texture components in smooth approximation and high frequency structural details. The subbands are processed separately by the prioritized patch-based inpainting with isophote energy driven texture synthesis at the core. The algorithm automatically estimates the wavelet coefficients of the target regions of various subbands using optimized patches from the surrounding DWT coefficients. The suggested performance improvement drastically improves execution speed over the existing algorithm. The proposed patch optimization strategy improves the quality of the fill. The fill-in is done with higher priority to structures and isophotes arriving at target boundaries. The effectiveness of the algorithm is demonstrated with natural and textured images with varying textural complexions.

Sharing a Large Secret Image Using Meaningful Shadows Based on VQ and Inpainting

  • Wang, Zhi-Hui;Chen, Kuo-Nan;Chang, Chin-Chen;Qin, Chuan
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.9 no.12
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    • pp.5170-5188
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    • 2015
  • This paper proposes a novel progressive secret image-hiding scheme based on the inpainting technique, the vector quantization technique (VQ) and the exploiting modification direction (EMD) technique. The proposed scheme first divides the secret image into non-overlapping blocks and categorizes the blocks into two groups: complex and smooth. The blocks in the complex group are compressed by VQ with PCA sorted codebook to obtain the VQ index table. Instead of embedding the original secret image, the proposed method progressively embeds the VQ index table into the cover images by using the EMD technique. After the receiver recovers the complex parts of the secret image by decoding the VQ index table from the shadow images, the smooth parts can be reconstructed by using the inpainting technique based on the content of the complex parts. The experimental results demonstrate that the proposed scheme not only has the advantage of progressive data hiding, which involves more shadow images joining to recover the secret image so as to produce a higher quality steganography image, but also can achieve high hiding capacity with acceptable recovered image quality.

A MODIFIED CAHN-HILLIARD EQUATION FOR 3D VOLUME RECONSTRUCTION FROM TWO PLANAR CROSS SECTIONS

  • Lee, Seunggyu;Choi, Yongho;Lee, Doyoon;Jo, Hong-Kwon;Lee, Seunghyun;Myung, Sunghyun;Kim, Junseok
    • Journal of the Korean Society for Industrial and Applied Mathematics
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    • v.19 no.1
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    • pp.47-56
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    • 2015
  • In this paper, we present an implicit method for reconstructing a 3D solid model from two 2D cross section images. The proposed method is based on the Cahn-Hilliard model for the image inpainting. Image inpainting is the process of reconstructing lost parts of images based on information from neighboring areas. We treat the empty region between the two cross sections as inpainting region and use two cross sections as neighboring information. We initialize the empty region by the linear interpolation. We perform numerical experiments demonstrating that our proposed method can generate a smooth 3D solid model from two cross section data.