Region Filling and Object Removal by ExemplarBased Image Inpainting

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Region Filling and Object Removal
byExemplar-Based Image Inpainting
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Introduction
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• 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.

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Key Observations

• A. Exemplar-Based Synthesis Suffices

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• B. Filling Order Is Critical

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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

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• 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

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

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• 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.

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• 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

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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
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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

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

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Results And Comparions
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Time
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Shape of the select
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Hand-draw
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Large object
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• END
• THANKS EVERYONE