Region Filling and Object Removal by ExemplarBased Image Inpainting - PowerPoint PPT Presentation

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Region Filling and Object Removal by ExemplarBased Image Inpainting

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A new algorithm is proposed for removing large objects from digital images. ... a color value (or 'empty,' if the pixel is unfilled) and a confidence value. ... – PowerPoint PPT presentation

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Title: Region Filling and Object Removal by ExemplarBased Image Inpainting


1
Region Filling and Object Removal
byExemplar-Based Image Inpainting
2
Introduction
3
  • A new algorithm is proposed for removing large
    objects from digital images.
  • this problem has been addressed by two classes of
    algorithms
  • 1) texture synthesis algorithms for
    generating large image regions from sample
    textures
  • 2) inpainting techniques for filling in
    small image gaps.

4
Key Observations
  • A. Exemplar-Based Synthesis Suffices

5
  • B. Filling Order Is Critical

6
Algorithm
  • Each pixel maintains a color value (or empty,
    if the pixel is unfilled) and a confidence value.
  • Algorithm iterates the following three steps
    until all pixels have been filled.
  • 1) Computing Patch Priorities
  • 2) Propagating Texture and Structure
    Information
  • 3) Updating Confidence Values

7
  • 1) Computing Patch Priorities
  • the priority computation is biased toward those
    patches which
  • 1) are on the continuation of strong edges.
  • 2) are surrounded by high-confidence pixels.
  • Given a patch centered at the point p for
    some , we define its priority as
    the product of two terms

8
  • C(p) the confidence term that measure of the
    amount of
  • reliable information surrounding the pixel p.

9
  • D(p) the data term that is a function of the
    strength of isophotes hitting the front
    at each iteration.
  • (1) np estimated as the unit vector orthogonal
    to the line
  • through the preceding and the successive
    points in the
  • list
  • (2) is computed as the maximum value of
    the
  • image gradient in .Robust filtering
    techniques may
  • also be employed here.

10
  • 2) Propagating Texture and Structure Information
  • propagate image texture by direct sampling of
    the source
  • region.
  • the distance between two generic
    patches and
  • is simply defined as the sum of squared
    differences

11
Synthesizing One Pixel
SAMPLE
Infinite sample image
Generated image
Instead of constructing a model, lets directly
search the input image for all such
neighbourhoods to produce a histogram for p
12
Really Synthesizing One Pixel
SAMPLE
finite sample image
Generated image
  • However, since our sample image is finite, an
    exact neighbourhood match might not be present
  • So we find the best match using SSD error
    (weighted by a Gaussian to emphasize local
    structure), and take all samples within some
    distance from that match

13
  • 3) Updating Confidence Values
  • After the patch has been filled with new
    pixel values,
  • the confidence is updated in the area
    delimited
  • by

14
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15
Results And Comparions
16
Time
17
Shape of the select
18
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19
Hand-draw
20
Large object
21
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22
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23
  • END
  • THANKS EVERYONE
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