Region Filling and Object Removal by

Exemplar-Based Image Inpainting

IntroductionIntroduction

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.

Key ObservationsKey Observations A. Exemplar-Based Synthesis Suffices

B. Filling Order Is Critical

AlgorithmAlgorithm Each pixel maintains a color value (or “empty,” if the p

ixel 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

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

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

D(p) the data term that is a function of the strength of isophotes hitting the front at each iteration. 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.

2) Propagating Texture and Structure Information propagate image texture by direct sampling of the sour

ce region.

the distance between two generic patches and is simply defined as the sum of squared differences

Synthesizing One PixelSynthesizing One Pixel

SAMPLE

Infinite sample image

Generated image

Instead of constructing a model, let’s directly search the

input image for all such neighbourhoods to

produce a histogram for p

Really Synthesizing One PixelReally 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

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

Results And ComparionsResults And Comparions

TimeTime

Shape of the selectShape of the select

Hand-drawHand-draw

Large objectLarge object

ENDEND

THANKS EVERYONETHANKS EVERYONE