Spatially varying PCA and BM3D (block matching

Report
Transform-based Non-local
Methods for Image Restoration
IT530, Lecture Notes
Non-local Techniques
Natural images have a great
deal of redundancy: patches
from different regions can be
very similar
NL-Means: a non-local pixelbased method
(Buades et al, 2005)
•Awate and Whitaker (PAMI 2007)
•Popat and Picard (TIP 1998)
•De-Bonet (MIT Tech report 1998)
•Wang et al (IEEE SPL 2003)
Difference
between
patches
2
Transform-based Techniques
DCT coefficients
of a “typical”
natural image
Observed property
of natural images:
Sparse coefficients
in Fourier/DCT/
Wavelet domain
3
Transform-based Techniques
DCT Coefficients
Manipulate
coefficients of
(absolute
value) coefficients
noisy
patch
of filtered patch
Get filtered patch by
inversion of the transform
from
Noisyfiltered
Patch coefficients
Repeat for all patches in sliding
window fashion: final filtered
Project
Patch onto
image obtained
by averaging
Transform
Basis
overlapping
filtered patches.
(DCT/Wavelet
Clean Patch etc.)
Transform
basis
Patch
matrix
Transform
Coefficients
4
Non-local transform based techniques
• Machine learning approach.
• Make use of non-local self-similarity at the
patch level to learn good transform bases for
denoising.
Non-local PCA
• PCA is the most basic transform to learn!
• Assume Gaussian noise (mean zero, known
variance).
• Given a patch (called “reference patch”) in a noisy
image, search for similar patches elsewhere in
the image.
• Given such a collection of patches, compute their
PCA bases.
• Compute the eigen-coefficients of the reference
patch.
Non-local PCA
• Manipulate these eigen-coefficients (by hardthresholding or Wiener filter).
• Reconstruct the reference patch.
• Repeat the procedure for every patch in the
noisy image in sliding window fashion.
• Average the multiple hypotheses that appear
at a pixel.
Ref: Muresan and Parks, “Adaptive principal
components for image denoising”, ICIP 2003.
Criterion: Patch Similarity
P1  Pref  z ~ N ( 0 ,  )
2
Two
patches:
identical
|| P1  P 2 ||
2
2
~

(
n
)
modulo
noise
2
2
P2  Pref  z ~ N ( 0 ,  )
Cumulative of chi-squared
is incomplete gamma
function.
2
x n
( ,
)
2 2
2
F ( x ; n )  GammaInc
2
2
2
2
n
3
n
1
|| P1  PP2 || if their
3 nsquared
patch
F ( 0A. 99
; P1) is considered similar to patch
ref

2
2
2
difference
is less than or2 
equal
to
2
2
3n 
2
8
Non-local PCA
y i  xi  ni , ni ~ N ( 0,  )
Noisy patch
The l-th coefficient of yi – computed by
projection onto the l-th local PCA
bases (obtained from similar patches)
l
We are modelling x i as zero-mean
Gaussian random variable with
variance  l
Two-stage non-local PCA
Ref: Zhang et al, Two stage image denoising by principal components
analysis with local pixel grouping
KSVD
BM3D
PCA
KSVD
BM3D
PCA
KSVD
BM3D
PCA
Time complexity
• The patches of size p x p are treated as vectors
of size p2 x 1.
• Hence the covariance matrices are of size p2 x
p2.
• Eigen-analysis will have a time complexity of
O(p6) per patch (not counting the time to
search for similar patches).
• This is quite expensive.
Non-local collaborative filtering: Block
Matching in 3D (BM3D)
• The state of the art method today.
• Based on the idea of non-local similarity at the
patch-level.
• Given a reference patch in the noisy image,
this method again collects similar patches.
• But this time, the similar patches and the
reference patch are arranged in the form of a
3D stack (of say some K patches in all).
Non-local collaborative filtering: Block
Matching in 3D (BM3D)
• The stack is projected onto 3D transform bases
(typically 3D DCT, or tensor product of 2D DCT
and 1D Haar wavelets).
• The 3D transform coefficients are manipulated –
usually by hard thresholding using the universal
threshold  2 log( p 2 K ).
• All the patches in the entire stack are
reconstructed using an inverse 3D transform.
• This is repeated for every patch in the image. The
multiple answers appearing at any pixel are
averaged.
BM3D – second stage
• The preceding steps form the first stage of BM3D.
• The output image of the first step is used to
compute patch similarities (this will be more
robust than computing the similarities in the
noisy image).
• Patches from the first-stage image are then
appropriately assembled into a stack.
• Corresponding patches from the noisy image are
assembled into a second stack.
BM3D – second stage
• 3D transform coefficients of both the stacks are
computed.
• The second stack is denoised using Wiener
filtering as follows:
2
c second
 stage
c stage  one

c
2
stage  one

2
c noisy
• This is again repeated in sliding-window fashion
with averaging.
Note: BM3D does allow individual patches from the
patch to be independently filtered. It filters the similar
patches from the stack collectively, assuming a
dependence between them! This is the reason for its
superior performance.
Results
• State of the art denoising method.
• Impressive results even at high noise levels.
• Residual images produced are very noisy.
• Preserves textures and fine details/edges very
well.
Noisy PSNR: 22.13
NL-SVD:
PSNR 30.88
SSIM 0.882
BM3D1:
PSNR 31.02
SSIM 0.884
BM3D2:
PSNR 31.66
SSIM 0.903
HOSVD:
PSNR 31.53
SSIM 0.897
NLMeans:
KSVD:
PSNR 30.762
29.42
SSIM 0.877
0.821
24
KSVD
HOSVD
BM3D2
BM3D1
NL-SVD
25
Noisy PSNR: 22.13
NL-SVD:
PSNR 30.187
SSIM 0.801
BM3D1:
PSNR 30.395
SSIM 0.809
BM3D2:
PSNR 30.8
SSIM 0.824
HOSVD:
PSNR 30.491
SSIM 0.814
NLMeans:
KSVD:
PSNR 30.36
28.91
SSIM 0.803
0.753
26
KSVD
HOSVD
BM3D2
BM3D1
NL-SVD
27
Gaussian Noise sigma = 15
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