Experimental results of the manuscript:
“Image restoration via Bayesian structured sparse coding: where structured sparsity
meets Gaussian scale mixture”
By Weisheng Dong, Guangming Shi, Yi Ma, and Xin Li,
submitted to International Journal of Computer Vision.
Note:
(1) The denoising method in [1] is labeled as “BM3DSAPCA”;
(2) The denoising method in [2] is labeled as “LSSC”;
(3) The denoising method in [3] labeled as “NCSR”.
Then, the denoised image by the method BSSC on image House is labeled as “BSSC_House”.
Other result images are labeled similarly.
Experiment 1: Image denoising
The deblurring results on Lena
The deblurring results on Monarch
The deblurring results on Barbara
The deblurring results on Boat
The deblurring results on C. Man
The deblurring results on Couple
The deblurring results on F. Print
The deblurring results on Hill
The deblurring results on House
The deblurring results on Man
The deblurring results on Peppers
The deblurring results on Straw
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Note:
(1) The deblurring method in [4] is labeled as “FISTA”;
(2) The deblurring method in [5] is labeled as “IDDBM3D”;
(3) The deblurring method in [3] is labeled as “NCSR”;
(4) The proposed denoising method is labeled as “BSSC”.
Then, the deblurred image by the method BSSC on image Butterfly is labeled as
“BSSC_Butterfly”. Other result images are labeled similarly.
Experiment 2: 9x9 uniform blur kernel,
The deblurring results on Butterfly
The deblurring results on Boats
The deblurring results on Cameraman
n  2
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The deblurring results on House
The deblurring results on Parrot
The deblurring results on Lena
The deblurring results on Barbara
The deblurring results on Starfish
The deblurring results on Peppers
The deblurring results on Leaves
Experiment 3:
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Gaussian blur kernel with standard deviation 1.6,  n  2
The deblurring results on Butterfly
The deblurring results on Boats
The deblurring results on Cameraman
The deblurring results on House
The deblurring results on Parrot
The deblurring results on Lena
The deblurring results on Barbara
The deblurring results on Starfish
The deblurring results on Peppers
The deblurring results on Leaves
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Note:
(1) The super-resolution method in [6] is labeled as “TV”;
(2) The super-resolution method in [7] is labeled as “Sparse”;
(3) The super-resolution method in [3] is labeled as “NCSR”;
(4) The proposed super-resolution method is labeled as “BSSC”.
Then, the high resolution (HR) image result by the method BSSC on image Girl is labeled as
“BSSC_Girl”. Other result images are labeled similarly.
Noiseless super-resolution:
The HR results with scalar factor 3 on Butterfly
The HR results with scalar factor 3 on Flower
The HR results with scalar factor 3 on Girl
The HR results with scalar factor 3 on Parthenon
The HR results with scalar factor 3 on Parrot
The HR results with scalar factor 3 on Raccoon
The HR results with scalar factor 3 on Bike
The HR results with scalar factor 3 on Hat
The HR results with scalar factor 3 on Plants
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The following results are in the cases that white Gaussian noise (WGN) with
standard deviation 5 is added to the LR images.
The HR results with scalar factor 3 on Butterfly
The HR results with scalar factor 3 on Flower
The HR results with scalar factor 3 on Girl
The HR results with scalar factor 3 on Parthenon
The HR results with scalar factor 3 on Parrot
The HR results with scalar factor 3 on Raccoon
The HR results with scalar factor 3 on Bike
The HR results with scalar factor 3 on Hat
The HR results with scalar factor 3 on Plants
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References:
[1] V. Katkovnik, A. Foi, K. Egiazarian, and J. Astola, “From local kernel to nonlocal
multiple-model image denoising,” Int. J. Comp. Vis., vol. 86, no. 1, pp. 1-32, Aug. 2010.
[2] J. Mairal, F. Bach, J. Ponce, G. Sapiro and A. Zisserman, “Non-Local Sparse Models for
Image Restoration,” in Proc. IEEE International Conference on Computer Vision, Tokyo,
Japan, 2009.
[3] W. Dong, L. Zhang, G. Shi, and X. Li, “Nonlocally centralized sparse representation for
image restoration,” IEEE Trans. on Image Processing, vol. 22, no. 4, pp. 1620-1630,
2013.
[4] A. Beck and M. Teboulle, “Fast gradient-based algorithms for constrained total variation
image denoising and deblurring problems,” IEEE Trans. On Image Process., vol. 18, no.
11, pp. 2419-2434, Nov. 2009.
[5] A. Danielyan, V. Katkovnik, and K. Egiazarian, “BM3D frames and variational image
deblurring,” IEEE Trans. On Image Processing, vol. 21, no. 4, Apr. 2012.
[6] A. Marquina, and S. J. Osher, “Image super-resolution by TV-regularization and
Bregman iteration,” J. Sci. Comput., vol. 37, pp. 367-382, 2008.
[7] J. Yang, J. Wright, Y. Ma, and T. Huang, “Image super-resolution as sparse
representation of raw image patches,” IEEE Computer Vision and Pattern Recognition,
vol. 1, pp. 1-8, Jun. 2008.