Transcript of Duke University COPYRIGHT © DUKE UNIVERSITY 2012 Sparsity Based Denoising of Spectral Domain...
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- Duke University COPYRIGHT DUKE UNIVERSITY 2012 Sparsity Based
Denoising of Spectral Domain Optical Coherence Tomography Images
Leyuan Fang, Shutao Li, Qing Nie, Joseph A. Izatt, Cynthia A. Toth,
and Sina Farsiu Biomedical Optics Express, 3(5), pp. 927-942, May,
2012 OCTNEWS.ORG Feature Of The Week 6/24/12 Leyuan.Fang@duke.edu
Vision and Image Processing Laboratory
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Laboratory Content 1. Introduction 2. Multiscale structural
dictionary 3. Non-local denoising 4. Results comparison 5. Software
display
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- COPYRIGHT DUKE UNIVERSITY 2012 Vision and Image Processing
Laboratory Introduction Two classic denoising frameworks: 1.
multi-frame averaging technique Low quality denoising result High
quality denoising result but requires higher image acquisition time
2. model-based single-frame techniques (e.g. Wiener filtering,
kernel regression, or wavelets)
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Laboratory Proposed Method Overview We introduce the Multiscale
Sparsity Based Tomographic Denoising (MSBTD) framework. MSBTD is a
hybrid more efficient alternative to the noted two classic
denoising frameworks applicable to virtually all tomographic
imaging modalities. MSBTD utilizes a non-uniform scanning pattern,
in which, a fraction of B-scans are captured slowly at a relatively
higher than nominal SNR. The rest of the B-scans are captured fast
at the nominal SNR. Utilizing the compressive sensing principles,
we learn a sparse representation dictionary for each of these
high-SNR images and utilize these dictionaries to denoise the
neighboring low-SNR B- scans.
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Laboratory Assumption In common SDOCT volumes, neighboring B-scans
have similar texture and noise pattern. summed-voxel projection
(SVP) en face SDOCT image B-Scan acquired from the location of the
blue line B-Scan acquired from the location of the yellow line
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Laboratory Sparse Representation SDOCT image or its patches
Dictionary to represent the SDOCT image Sparse coefficients Our
paradigm: Learn the dictionary from the neighboring high-SNR B-scan
How to learn the dictionary? Train by K-SVD Train by PCA Classic
paradigm: Learn the dictionary directly from the noisy image
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Laboratory Multiscale structural dictionary To better capture the
properties of structures and textures of different size, we utilize
a novel multi-scale variation of the structural dictionary
representation.
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Laboratory Non-local strategy To further improve the performance,
we search for the similar patches in the SDOCT images and average
them to achieve better results. The MSTBD denoising process
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Laboratory Results comparison Quantitative measures 1.
Mean-to-standard-deviation ratio (MSR) where and are the mean and
the standard deviation of the foreground regions 2.
Contrast-to-noise ratio (CNR) where and are the mean and the
standard deviation of the background regions 3. Peak
signal-to-noise-ratio (PSNR) where is the h th pixel in the
reference noiseless image, represents the h th pixel of the
denoised image, is the total number of pixels, and is the maximum
intensity value of
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Laboratory Results comparison Experiment 1: denoising (on normal
subject image) based on learned dictionary from a nearby high-SNR
Scan Averaged image Noisy image (Normal subject) Result using the
Tikhonov method [1] MSR = 10.64, CNR = 3.90 MSR = 3.20, CNR = 1.17
MSR = 7.65, CNR = 3.25, PSNR = 23.35 Result using the NEWSURE
method [2] Result using the KSVD method [3] Result using the BM3D
method [4] MSR = 7.85, CNR = 2.87, PSNR = 24.51 MSR = 13.26, CNR =
5.19, PSNR = 28.48 MSR = 11.96, CNR = 4.72, PSNR = 28.35 Result
using the MSBTD method MSR = 15.41, CNR = 5.98, PSNR = 28.83 [1] G.
T. Chong, et al., Abnormal foveal morphology in ocular albinism
imaged with spectral-domain optical coherence tomography, Arch.
Ophthalmol. (2009). [2] F. Luisier, et al., A new SURE approach to
image denoising: Interscale orthonormal wavelet thresholding, IEEE
Trans. Image Process (2007). [3] M. Elad, et al., Image denoising
via sparse and redundant representations over learned dictionaries,
IEEE Trans. Image Process. (2006). [4] K. Dabov, et al., Image
denoising by sparse 3-D transform-domain collaborative filtering,
IEEE Trans. Image Process. (2007).
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Laboratory Results comparison Experiment 1: denoising (on dry AMD
subject image) based on learned dictionary from a nearby high-SNR
Scan Averaged image Noisy image (AMD subject) Result using the
Tikhonov method [1] MSR = 10.20, CNR = 3.75 MSR = 3.46, CNR = 1.42
MSR = 8.12, CNR = 3.92, PSNR = 21.76 Result using the NEWSURE
method [2] Result using the KSVD method [3] Result using the BM3D
method [4] MSR = 8.04, CNR = 3.39, PSNR = 23.87 MSR = 12.82, CNR =
5.62, PSNR = 26.07 MSR = 12.08, CNR = 5.31, PSNR = 25.68 Result
using the MSBTD method MSR = 15.28, CNR = 6.45, PSNR = 26.11 [1] G.
T. Chong, et al., Abnormal foveal morphology in ocular albinism
imaged with spectral-domain optical coherence tomography, Arch.
Ophthalmol. (2009). [2] F. Luisier, et al., A new SURE approach to
image denoising: Interscale orthonormal wavelet thresholding, IEEE
Trans. Image Process (2007). [3] M. Elad, et al., Image denoising
via sparse and redundant representations over learned dictionaries,
IEEE Trans. Image Process. (2006). [4] K. Dabov, et al., Image
denoising by sparse 3-D transform-domain collaborative filtering,
IEEE Trans. Image Process. (2007).
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Laboratory Results comparison Experiment 2: denoising based on
learned dictionary from a distant high-SNR scan Summed-voxel
projection (SVP) en face image Noisy image acquired from the
location b MSR = 3.10, CNR = 1.01 Result using the KSVD method [1]
Result using the BM3D method [2] Result using the MSBTD method MSR
= 13.93, CNR = 5.03 MSR = 14.93, CNR = 5.46 MSR = 18.57, CNR = 6.88
[1] M. Elad, et al., Image denoising via sparse and redundant
representations over learned dictionaries, IEEE Trans. Image
Process. (2006). [2] K. Dabov, et al., Image denoising by sparse
3-D transform-domain collaborative filtering, IEEE Trans. Image
Process. (2007).
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Laboratory Results comparison Experiment 2: denoising based on
learned dictionary from a distant high-SNR scan Summed-voxel
projection (SVP) en face image Noisy image acquired from the
location c MSR = 3.30, CNR = 1.40 Result using the KSVD method [1]
Result using the BM3D method [2] Result using the MSBTD method MSR
= 10.30, CNR = 4.95 MSR = 9.91, CNR = 4.70 MSR = 11.71, CNR = 5.35
[1] M. Elad, et al., Image denoising via sparse and redundant
representations over learned dictionaries, IEEE Trans. Image
Process. (2006). [2] K. Dabov, et al., Image denoising by sparse
3-D transform-domain collaborative filtering, IEEE Trans. Image
Process. (2007).
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Laboratory Software display MATLAB based MSBTD software &
dataset is freely available at
http://www.duke.edu/~sf59/Fang_BOE_2012.htm Input the test image
Input the Averaged image Setting the parameters for the MSBTD Run
the MSBTD Run the Tikhonov Save the results Select region from the
test image Select a background region from the test image Select a
foregournd region from the test image
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Laboratory CLICK ON THE GUI TO PLAY VIDEO DEMO OF THE SOFTWARE
MATLAB based MSBTD software & dataset is freely available at
http://www.duke.edu/~sf59/Fang_BOE_2012.htm