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Image Analogies

Image Analogies. Shitong Jia. What is image analogies?. In an image analogy process, the transformation between two images A and A' is "learned". Later, given a different image B, it's "analogy" image B' can be generated based on the learned transformation.

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Image Analogies

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  1. Image Analogies Shitong Jia

  2. What is image analogies? In an image analogy process, the transformation between two images A and A' is "learned". Later, given a different image B, it's "analogy" image B' can be generated based on the learned transformation. ——wikipedia Briefly, If we are given A:A':: B:_ , we can get A:A':: B:B' but avoid calculating complex and non-linear filters.

  3. : :: A' A : B B'

  4. Main problem: How to find the best pixel to be copied to B'? The method: Paper: Image Analogies (Hertzmann, 2011)

  5. The pseudocode for the algorithm in paper

  6. How to find the match? 1. Build the features a.Transferring images into YIQ color space and use the data in Y(luminance) channel. b.Building the Guassian Pyramid for A, A' and B. c. Creating a feature vector which contains the neighbor pixels of the pixel in both current level and last coarse level.

  7. Another point: Luminance remapping (Only for the color artistic filters) If image A is light and image B is dark, or the difference between their color is large, there will be fewer matches. Applying a Luminance remapping in Y channel can improve the result. For both A and A'

  8. Fecture vector for A and A': Fine level: Al: 5*5 pixels Al': L-shape 12 pixels Coarse level: Al-1: 3*3 pixels Al-1': 3*3 pixels The same as B

  9. How to find the match? 2. Search function a.Approximate-nearest-neighbor search (ANN) (p_app) b. Best coherence match (p_coh) Then compare them: if d_coh ≤ d_app(1 + 2^(l-L)*Kappa) then return p_coh else return p_app

  10. Approximate-nearest-neighbor search (ANN) In the nearest neighbor problem a set of data points' features in is given. These datum are preprocessed into a data structure, so that given any query point q, the nearest or generally k nearest points of P to q can be reported efficiently. Fast library of ANN (provided by University of British Columbia) This library uses a KD tree to accelerate the process of finding a nearest neighbor in the high dimensional space of the feature vectors. Much faster than brute force but not get the closest pixel.

  11. Get best coherence match For each neighborhood pixel of the current pixel in B, find the patch with the correspong position of that pixel in A. Find the the patch with smallest Sum of squared differences with the patch in A. Then copy the pixel in the corresponding A' to B'. B

  12. Then recovering the color simply by copying the I and Q channels of the input A'(sometimes is the pixel in B) image into the synthesized B' image.Finally transferring back to RGB.

  13. Things to improve: 1.The algorithm is slow, with several levels of gaussian pyramid and coherence match. 2.Better operation for the holes. 3.How to choose the suitable Kappa?

  14. without changing to YIQ Some results A' A l=3 B result in paper l=1 apply coherence match kappa=5 texture-by-numbers

  15. l=3 image filter(emboss)

  16. without get coherence match l=3 with get coherence match l=1 color artistic filter

  17. Image colorization

  18. Thank you!

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