文件列表:

BPFA_Inpainting_04152010 (0, 2010-04-25)
BPFA_Inpainting_04152010\barbara256.png (43807, 2010-02-14)
BPFA_Inpainting_04152010\BPFA_Inpainting.m (9339, 2010-04-13)
BPFA_Inpainting_04152010\castle.png (231141, 2010-04-04)
BPFA_Inpainting_04152010\Demo_Denoise_Inpainting_GRAY.m (3062, 2010-04-25)
BPFA_Inpainting_04152010\Demo_Inpainting_GRAY.m (3138, 2010-04-25)
BPFA_Inpainting_04152010\Demo_Inpainting_HyperSpecImage.m (2430, 2010-04-25)
BPFA_Inpainting_04152010\Demo_Inpainting_RGB.m (3525, 2010-04-25)
BPFA_Inpainting_04152010\DenoiseOutput_LowMemoryReq.m (1851, 2010-03-09)
BPFA_Inpainting_04152010\DispDictionary.m (2369, 2010-03-10)
BPFA_Inpainting_04152010\house.png (34985, 2010-04-03)
BPFA_Inpainting_04152010\idexUpdate.m (767, 2010-03-08)
BPFA_Inpainting_04152010\InitMatrix_MissingData.m (1492, 2010-04-14)
BPFA_Inpainting_04152010\R1.mat (5440767, 2009-10-28)
BPFA_Inpainting_04152010\Samplealpha.m (900, 2010-03-10)
BPFA_Inpainting_04152010\SampleDZS_MissingData.m (6226, 2010-03-31)
BPFA_Inpainting_04152010\Samplephi.m (326, 2009-10-26)
BPFA_Inpainting_04152010\SamplePi.m (254, 2010-03-09)
BPFA_Inpainting_04152010\sparse_mult.m (433, 2010-04-26)
BPFA_Inpainting_04152010\sparsify.m (220, 2009-10-29)
BPFA_Inpainting_04152010\SZUpdate.m (649, 2010-02-08)
BPFA_Inpainting_04152010\Update_Input_MissingData.m (753, 2010-03-09)

Matlab code for the paper: M. Zhou, H. Chen, J. Paisley, L. Ren, L. Li, Z. Xing, D. Dunson, G. Sapiro and L. Carin, Nonparametric Bayesian Dictionary Learning for Analysis of Noisy and Incomplete Images, submitted. Coded by: Mingyuan Zhou, ECE, Duke University, mz1@ee.duke.edu, mingyuan.zhou@duke.edu File list: Demo files: Demo_Inpainting_GRAY.m: Running file for BPFA gray-scale image inpainting. Demo_Inpainting_RGB.m: Running file for BPFA RGB image inpainting. Demo_Inpainting_HyperSpecImage.m: Running file for BPFA Hyperspectral image inpainting. Demo_Denoise_Inpainting_GRAY.m: Running file for gray-scale image simultaneous denoising & inpainting Main programs: BPFA_Inpainting.m: The BPFA image inpainting program. Subprograms for Gibbs sampling: SampleDZS_Missingdata.m: Sampling the dictionary D, the binary indicating matrix Z, and the pseudo weight matrix S. Used for missing data case. SamplePi.m: Sampling Pi. Samplephi.m: Sampling the noise precision phi. Samplealpha: Sampling alpha, the precision of si. Subprograms for the updates in sequential learning idexUpdate.m: Update the index of the training data in the input data matrix X. Update_Input_Missingdata.m: Update the training data set in sequential learning. Used for missing data case. SZUpdate.m: Update the pseudo weight matrix S and binary indicating matrix Z in sequential learning. Other subprograms: sparsity.m: Squeeze out zero components in the sparse matrix. DispDictionary.m: Display the dictionary elements as a image DenoiseOutput_LowMemoryReq.m: Reconstruct the image InitMatrix_Denoise.m: Initialization for gray-scale image denoising sparse_mult.m: Use sparse multiplication to eliminate unnecessary computation house.png: the original house image. barbara256.png: the barbara256 image. Other gray scale test images can be downloaded from http://www.cs.tut.fi/~foi/GCF-BM3D/ castle.png: the original castle image. Other RGB test images can be found in The Berkeley Segmentation Dataset and Benchmark. R1.mat: a 150*150*210 hyperspectral urban image. Papers and test results related to the code can be found at http://people.ee.duke.edu/~mz1/

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