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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23
• END
• THANKS EVERYONE