Edge-Optimized A-Trous Wavelets for Local Contrast ...€¦ · Page 6 EOW for local contrast and...
Transcript of Edge-Optimized A-Trous Wavelets for Local Contrast ...€¦ · Page 6 EOW for local contrast and...
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Edge-Optimized A-Trous Wavelets for Local ContrastEnhancement with Robust Denoising
Johannes Hanika, Holger Dammertz, Hendrik Lensch |September 17, 2011
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Page 2 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Motivation: Edge-Aware Image Processing
ongoing research:
look transfer via bilateral filtering (Dae, Paris and Durand 2006)
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Page 2 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Motivation: Edge-Aware Image Processing
ongoing research:
multi scale decomposition by solving a linear system (Farbman et al. 2008)
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Page 2 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Motivation: Edge-Aware Image Processing
ongoing research:
colorization via edge-avoiding wavelets (Fattal 2009)
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Page 2 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Motivation: Edge-Aware Image Processing
ongoing research:
local contrast via local histograms (Kass 2010)
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Page 2 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Motivation: Edge-Aware Image Processing
ongoing research:
via domain transform (Gastal and Oliveira 2011)
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Page 3 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Previous Work
all based on multiscale decompositions:
iteratively applying a bilateral filter lots of techniques to speed it up still high memory footprint and/or low performance
high quality by solving a linear system not meant to be high performance
fastest methods based on decimated wavelets (Fattal 2009)
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Page 4 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Previous Work
decimated wavelets fail to capture edges at all scales:
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Page 4 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Previous Work
decimated wavelets fail to capture edges at all scales:
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Page 4 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Previous Work
because coarse coefficients are sparse
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Page 5 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Previous Work
use a-trous wavelet
i=0i=1i=2
results in a full image (not decimated) per step
⇒ the transformation is shift invariant
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Page 6 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Previous Work
a-trous wavelet decomposition
1. level i = 0 starts with the input signal c0(p)
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Page 6 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Previous Work
a-trous wavelet decomposition
1. level i = 0 starts with the input signal c0(p)2. compute next base layer (convolution with holes)
ci+1(p) =1
k∑q∈Ω
hi (q) · ci (p)
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Page 6 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Previous Work
a-trous wavelet decomposition
1. level i = 0 starts with the input signal c0(p)2. compute next base layer (convolution with holes)
ci+1(p) =1
k∑q∈Ω
hi (q) · ci (p)
3. compute next detail layer (difference)
di (p) = ci (p)− ci+1(p)
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Page 6 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Previous Work
a-trous wavelet decomposition
1. level i = 0 starts with the input signal c0(p)2. compute next base layer (convolution with holes)
ci+1(p) =1
k∑q∈Ω
hi (q) · ci (p)
3. compute next detail layer (difference)
di (p) = ci (p)− ci+1(p)
4. if i < N : i := i+1; goto 2
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Page 6 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Previous Work
a-trous wavelet decomposition
1. level i = 0 starts with the input signal c0(p)2. compute next base layer (convolution with holes)
ci+1(p) =1
k∑q∈Ω
hi (q) · ci (p)
3. compute next detail layer (difference)
di (p) = ci (p)− ci+1(p)
4. if i < N : i := i+1; goto 25. d0,d1, ...,dN−1,cN is the wavelet transform of c.
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Page 6 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Previous Work
a-trous wavelet decomposition edge-aware version
1. level i = 0 starts with the input signal c0(p)2. compute next base layer (convolution with holes)
ci+1(p) =1
k∑q∈Ω
hi (q) · ci (p)·wσr(p,q)
3. compute next detail layer (difference)
di (p) = ci (p)− ci+1(p)
4. if i < N : i := i+1; goto 25. d0,d1, ...,dN−1,cN is the wavelet transform of c.
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Page 6 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Previous Work
a-trous wavelet decomposition edge-aware version
1. level i = 0 starts with the input signal c0(p)2. compute next base layer (convolution with holes)
ci+1(p) =1
k∑q∈Ω
hi (q) · ci (p)·wσr(p,q)
3. compute next detail layer (difference)
di (p) = ci (p)− ci+1(p)
4. if i < N : i := i+1; goto 25. d0,d1, ...,dN−1,cN is the wavelet transform of c.
synthesis: simply add up base and detail layers
c = cN +0
∑i=N−1
di .
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Page 7 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Decomposition
example coarse and detail layersc4 d1 d2 d3 d4
vanilla
edge-aw
are
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Page 8 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Synthesis for Local Contrast
add up boosted detail layers
c = cN +0
∑i=N−1
βi ·di .
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Page 9 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Observation
how to choose good edge weights wσr (p,q)?
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Page 9 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Observation
how to choose good edge weights wσr (p,q)?
too strong edge weights: gradient reversals
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Page 9 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Observation
how to choose good edge weights wσr (p,q)?
too strong edge weights: gradient reversals
too soft edge weights: halos
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Page 9 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Observation
how to choose good edge weights wσr (p,q)?
too strong edge weights: gradient reversals
too soft edge weights: halos
Kass and Solomon (2010) do explicit diffusion on coarse buffer as post
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Page 10 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Decomposition is fast!
⇒ optimization by synthesis to acquire σr per pixel!
stay in wavelet framework
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Page 11 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Edge-Optimized Decomposition
at each scale, do several decompositions using σjr , j = 0,1, ...
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Page 11 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Edge-Optimized Decomposition
at each scale, do several decompositions using σjr , j = 0,1, ...
compute error measure ej
ej = d2i ,j +λ · ‖∇ci ,j‖
prefer low energy in details d and smooth base layer c
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Page 11 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Edge-Optimized Decomposition
at each scale, do several decompositions using σjr , j = 0,1, ...
compute error measure ej
ej = d2i ,j +λ · ‖∇ci ,j‖
prefer low energy in details d and smooth base layer c
choose per-pixel edge weight
σkr (p) : k = argminjej
details how to make noisy estimates of ∇c stable in the paper
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Page 12 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Edge-Optimized Decomposition
error images ej
choice of σr and input image
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Page 13 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Decomposition Quality
input
colored output for visualization as (Farbman 08)edge-avoiding bilateral
(Farbman 08) WLS edge-optimized
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Page 13 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Decomposition Quality
input
colored output for visualization as (Farbman 08)edge-avoiding bilateral
(Farbman 08) WLS edge-optimized
comparable quality,orders of magnitudefaster
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Page 14 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Synthesis with Denoising
synthesis after local contrast boost also boosts noise!
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Page 14 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Synthesis with Denoising
synthesis after local contrast boost also boosts noise!
wavelet framework ⇒ can use robust noise variance estimate andBayesShrink threshold
d ′i = max0, |di |−T · sign(di )
and ci−1 = ci +β ·d ′i
details in the paper
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Page 15 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Denoising Quality
input 5% noise a-trous PSNR 32.5
EAW PSNR 39.1 EOW PSNR 39.8
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Page 15 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Denoising Quality
input 10% noise a-trous PSNR 26.3
EAW PSNR 34.6 EOW PSNR 35.9
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Page 15 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Denoising Quality
input 40% noise a-trous PSNR 26.5
EAW PSNR 15.0 EOW PSNR 19.6
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Page 16 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Performance (CPU)
algorithm wallclockEAW (Fattal 09) (core i7, α = 1) 0.088sEAW (Fattal 09) (core i7, α = 0.8) 0.296sthis paper (core i7) 0.197s
1 megapixel, 3 scales, 4 channels per pixel Lab data
core i7 CPU : 8 threads on 4 cores
(Fattal 09) with α = 1 removes expensive exponentiation
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Page 17 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Performance (GPU)
ms number of σr tested1 2 3 4 5
1 scale 19 23 26 32 392 scales 27 35 43 51 633 scales 35 48 61 75 874 scales 42 61 81 102 1205 scales 55 80 109 134 163
edge-optimized wavelet transform on a GTX480 for a one megapixel image
numbers are in milliseconds
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Page 18 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Results (Local Contrast)
(video)
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Page 19 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Limitations
high contrast, axis aligned changes (in hdr images) can lead to aliasing:
transparently reduced by our optimization (both via d2 and smoothness term)
technique to further ameliorate that in the paper
not the world’s best denoising technique, but helps suppress noiseenhancement during local contrast step
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Page 20 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Summary
edge avoiding a-trous wavelets are useful!
they can be fast (suitable for video processing)
and achieve high-quality coarse/detail decompositions avoid gradient reversals avoid halos better match the assumptions of BayesShrink denoising
parameter free, if you want it
super simple to implement
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Page 21 EOW for local contrast and denoising | Johannes Hanika, Holger Dammertz, Hendrik Lensch | September 17, 2011
Thank you for listening!
some of the code is available at http://darktable.sf.net(hardcore SSE optimized + OpenCL)
thanks to Edouard Gomez and Rostyslav Pidgornyi for the fast SSEversion!