Region filling and object removal by exemplar-based image inpainting

@article{Criminisi2004RegionFA,
  title={Region filling and object removal by exemplar-based image inpainting},
  author={Antonio Criminisi and Patrick P{\'e}rez and Kentaro Toyama},
  journal={IEEE Transactions on Image Processing},
  year={2004},
  volume={13},
  pages={1200-1212}
}
A new algorithm is proposed for removing large objects from digital images. The challenge is to fill in the hole that is left behind in a visually plausible way. In the past, this problem has been addressed by two classes of algorithms: 1) "texture synthesis" algorithms for generating large image regions from sample textures and 2) "inpainting" techniques for filling in small image gaps. The former has been demonstrated for "textures"-repeating two-dimensional patterns with some stochasticity… 

Region Filling and Object Removal by Exemplar- Based Image Inpainting

A new algorithm is proposed for removing large objects from digital images and filling in the hole that is left behind in a visually plausible way using "inpainting" techniques for filling in small image gaps.

Region Filling and Object Removal by Exemplar-Based Image Inpainting

A new algorithm is proposed for removing large objects from digital images by combining the advantages of “texture synthesis” and “inpainting” techniques for painting in small image gaps.

An Algorithm For Object Removal And Image Completion Using Exemplar-Based Image Inpainting

A robust algorithm that combines these two methods for removing large objects from digital images and filling up the hole by using background information in a visually plausible way is presented.

Exemplar Based Image Inpainting to remove object

This paper proposes a best algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting, and achieves simultaneous propagation of texture and structure information by a single, efficient algorithm.

Region Filling and Object Removal by using Criminisis Algorithm

A new algorithm is proposed for removing large objects from digital images that combines the advantages of these two approaches key wordss—Component, formatting, style, styling, insert.

Patch-Based Inpainting for Object Removal and Region Filling in Images

A new approach that allows the simultaneous filling in of different structures and textures is discussed in this present study, which significantly improve execution speed compared with pixel-based filling.

Survey on Image Inpainting Techniques: Texture Synthesis, Convolution and Exemplar Based Algorithms

This paper summarizes the combination of these algorithms and proposes a new Hybrid Inpainting Algorithm for inpainting large as well as small regions in less time.

A NEW TEXTURE SYNTHESIS BASED IMAGE RESTORATION APPROACH WITH IMAGE FILTER

Inpainting means filling of areas/regions of an image or video in such a way that the modified region(s) is visually agreeable to human eye and Restoration of a damaged/historical image has always been an important part of image processing.

Image inpainting on satellite image using texture synthesis & region filling algorithm

  • Geeta K. SarpateShanti K. Guru
  • Computer Science
    2014 International Conference on Advances in Communication and Computing Technologies (ICACACT 2014)
  • 2014
An algorithm was proposed to synthesize the structure & texture as well as fill the hole that is left behind in an undetectable form of image inpainting, and an attempt has been made to compute actual color values using exemplar based texture synthesis and region filling method.

Fast and Enhanced Algorithm for Exemplar Based Image Inpainting

An algorithm is presented that improves and extends a previously proposed algorithm and provides faster in painting and can in paint large regions as well as recover small portions of the image.
...

References

SHOWING 1-10 OF 30 REFERENCES

Object removal by exemplar-based inpainting

  • A. CriminisiP. PérezK. Toyama
  • Computer Science
    2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.
  • 2003
A best-first algorithm in which the confidence in the synthesized pixel values is propagated in a manner similar to the propagation of information in inpainting, which demonstrates the effectiveness of the algorithm in removing large occluding objects as well as thin scratches.

Simultaneous structure and texture image inpainting

The novel contribution of the paper is the combination of these three previously developed components: image decomposition with inpainting and texture synthesis, which permits the simultaneous use of filling-in algorithms that are suited for different image characteristics.

Image inpainting

A novel algorithm for digital inpainting of still images that attempts to replicate the basic techniques used by professional restorators, and does not require the user to specify where the novel information comes from.

Image replacement through texture synthesis

This work proposes a technique based on Heeger and Bergen's texture synthesis algorithm (1995), which can be substituted with a synthetic texture derived from another portion of the image by integrating a composition step into the aforementioned algorithm.

A Variational Model for Filling-In Gray Level and Color Images

A variational approach for filling-in regions of missing data in gray-level and color images is introduced, based on joint interpolation of the image gray-levels and gradient/isophores directions, smoothly extending in an automatic fashion the isophote lines into the holes ofMissing data.

Synthesizing natural textures

The algorithm is extended to allow direct user input for interactive control over the texture synthesis process, which allows the user to indicate large-scale properties of the texture appearance using a standard painting-style interface, and to choose among various candidate textures the algorithm can create by performing different number of iterations.

Fragment-based image completion

A new method for completing missing parts caused by the removal of foreground or background elements from an image, iteratively approximating the unknown regions and composites adaptive image fragments into the image to synthesize a complete, visually plausible and coherent image.

On pixel-based texture synthesis by non-parametric sampling

Image analogies

This paper describes a new framework for processing images by example, called “image analogies,” based on a simple multi-scale autoregression, inspired primarily by recent results in texture synthesis.

A Non-Hierarchical Procedure for Re-Synthesis of Complex Textures

It is shown that the accurate reproduction of features in the input texture depends on the order in which pixels are added to the output image, which is capable of reproducing large features even if only the interactions of nearby pixels are considered.