real-time exemplar-based face sketch synthesis
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Real-Time Exemplar-Based Face Sketch Synthesis
Pipeline illustration
Note: containing animations
Yibing Song1 Linchao Bao1 Qingxiong Yang1 Ming-Hsuan Yang2
1City University of Hong Kong2University of California at Merced
Our assumption: a database containing photo-sketch pairs
1. photo database 2. sketch database
Aligned
Coarse Sketch GenerationStep 1: KNN search
p
Test photo patch Test photo
Training photo dataset
π» ππ» π
π» π
Matched photo patch
Relative position
Similarly
Matched photo patch
Relative position
βππ²[ ]βπ =
Test photo patch
Matched photo patch
Matched photo patch
Matched photo patch
πππ β +ππ
π β +πππ² β ΒΏ
2. Compute linear mapping function defined by
Coarse Sketch GenerationStep 2: Linear Estimation from Photos
Matched sketch pixel
p
Matched sketch pixel
Test photo
πΊπ· +βππ
β
πΊπ· +βππ
β
Matched sketch pixelπΊπ· +βππ²
β
πππ β +ππ
π β +πππ² β ΒΏ
Estimation on pixel p
Repeat for every pixel
Coarse sketch
Coarse Sketch GenerationStep 3: Apply Linear Mapping to Sketches
π¬π
Because: coarse sketch image is not natural. is not a good similarity measurement between p and r.
Denoising: State-of-the-art Image Denoising Algorithms
Coarse sketch
Nonlocal Means (NLM)
p
r
ππππΏπ=ΒΏ πΈπ
π€(π ,π )+β―
For all pixels in the neighbor of p:
Little improvement
After NLM
q
πΈππ€(π ,π)+ΒΏ
[NLM] A. Buades, B. Coll and J.-M. Morel, A non-local algorithm for image denoising, CVPR 2005.
Motivation β BM3D
BM3D groups correlated patches in the noisy image to create multiple estimations.
Our idea for sketch denoising: group highly similar sketch estimations.
How BM3D works
[BM3D] K. Dabov, A. Foi, V. Katkovnik, and K. Egiazarian, βImage denoising by sparse 3D transform-domain collaborative filtering,β IEEE Trans. Image Process., vol. 16, no. 8, pp. 2080-2095, August 2007.
π€(π ,π) β
Proposed Spatial Sketch Denoising Algorithm (SSD)
Test photo
q
πΊπ+βππ
β
p
Matched sketch
πΊπ+βππ
β
Similarly ,
πΊπ+βππ
β
πΊπ+βππ
β
,
πΊπ+βππ²
β
πΊπ+βππ²
βπππ β +ππ
π β +πππ² β ΒΏ
π¬ππ
p
Estimations from pixels in local region
r π¬ππ
Averaging estimations to generate output sketch value.
Nonlocal Means (NLM):
ππππΏπ=ΒΏ πΈπ +β―πΈππ€(π ,π )β+ΒΏ
Proposed SSD:
πππππ·=ΒΏ +β―+ΒΏ1 βπ¬π
π 1 βπ¬ππ
p
Proposed SSD is robust to
Input 5x5 local region
11x11 local region
17x17 local region
23x23 local region
Note: When is sufficient large (i.e., >100), the proposed SSD can effectivelysuppress noise while preserving facial details like the tiny eye reflections (see close-ups).
Robustness to the region size - the only parameter involved
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