文件列表:

BPFA_Denoising_Inpainting_codes_Inference_10292009\barbara.png (185727, 2009-05-27)
BPFA_Denoising_Inpainting_codes_Inference_10292009\BPFA_Denoise.m (6284, 2009-10-29)
BPFA_Denoising_Inpainting_codes_Inference_10292009\BPFA_Denoising.m (5995, 2009-10-30)
BPFA_Denoising_Inpainting_codes_Inference_10292009\BPFA_Inpainting.m (6870, 2009-10-30)
BPFA_Denoising_Inpainting_codes_Inference_10292009\castle_damaged.png (150614, 2009-05-18)
BPFA_Denoising_Inpainting_codes_Inference_10292009\castle_original.png (231141, 2009-05-18)
BPFA_Denoising_Inpainting_codes_Inference_10292009\Demo_Denoise.m (2378, 2009-10-29)
BPFA_Denoising_Inpainting_codes_Inference_10292009\Demo_Denoising.m (2380, 2009-10-30)
BPFA_Denoising_Inpainting_codes_Inference_10292009\Demo_Inpainting_GRAY.m (3048, 2009-10-29)
BPFA_Denoising_Inpainting_codes_Inference_10292009\Demo_Inpainting_RGB.m (3737, 2009-10-30)
BPFA_Denoising_Inpainting_codes_Inference_10292009\DenoiseOutput.m (1074, 2009-10-29)
BPFA_Denoising_Inpainting_codes_Inference_10292009\DispDictionary.m (2250, 2009-10-29)
BPFA_Denoising_Inpainting_codes_Inference_10292009\fig13_damaged.bmp (390906, 2009-05-18)
BPFA_Denoising_Inpainting_codes_Inference_10292009\fig13_damaged.png (73627, 2009-05-18)
BPFA_Denoising_Inpainting_codes_Inference_10292009\fig13_original.png (242630, 2009-05-18)
BPFA_Denoising_Inpainting_codes_Inference_10292009\house.png (34985, 2009-05-27)
BPFA_Denoising_Inpainting_codes_Inference_10292009\idexUpdate.m (604, 2009-10-26)
BPFA_Denoising_Inpainting_codes_Inference_10292009\InitMatrix.m (1710, 2009-10-28)
BPFA_Denoising_Inpainting_codes_Inference_10292009\Mingyuan_nips2009_final.pdf (719812, 2009-10-29)
BPFA_Denoising_Inpainting_codes_Inference_10292009\Model and Inference.pdf (100538, 2009-10-28)
BPFA_Denoising_Inpainting_codes_Inference_10292009\Samplealpha.m (312, 2009-10-29)
BPFA_Denoising_Inpainting_codes_Inference_10292009\SampleDZS.m (4918, 2009-10-28)
BPFA_Denoising_Inpainting_codes_Inference_10292009\SampleDZS_MissingData.m (8974, 2009-10-28)
BPFA_Denoising_Inpainting_codes_Inference_10292009\Samplephi.m (326, 2009-10-26)
BPFA_Denoising_Inpainting_codes_Inference_10292009\SamplePi.m (188, 2009-10-26)
BPFA_Denoising_Inpainting_codes_Inference_10292009\Solve out of memory error.txt (1079, 2009-10-29)
BPFA_Denoising_Inpainting_codes_Inference_10292009\sparsify.m (220, 2009-10-29)
BPFA_Denoising_Inpainting_codes_Inference_10292009\SZUpdate.m (521, 2009-10-28)
BPFA_Denoising_Inpainting_codes_Inference_10292009\XUpdate.m (325, 2009-10-26)
BPFA_Denoising_Inpainting_codes_Inference_10292009\XUpdate_MissingData.m (696, 2009-10-28)
BPFA_Denoising_Inpainting_codes_Inference_10292009\YflagUpdate.m (827, 2009-10-29)
BPFA_Denoising_Inpainting_codes_Inference_10292009 (0, 2009-10-30)

File List: Mingyuan_nips2009_final.pdf: the final version of the paper "Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations," Neural Information Processing Systems (NIPS), 2009. Model and Inference.pdf: The model and inference for the above paper. house.png: the orignial house image. barbara.png: the original barbara image. castle_damaged.png: the castle image with 80% pixels missing. castle_original.png: the orignial castle image. fig13_damaged.png, fig13_damaged.bmp: the New Orleans image corrupted by text. fig13_original.png: the original New Orleans image. Solve out of memory error.txt: Instructions for allowing Matlab to manage 3GB memory instead of the default 2GB memory in Windows XP. Demo files: Demo_Denoising.m: Running file for BPFA image denoising. Demo_Inpainting_GRAY.m: Running file for BPFA gray scale image inpainting. Demo_Inpainting_RGB.m: Running file for BPFA rgb image inpainting. Main programs: BPFA_Denoising.m: The BPFA grayscale image denoising program. BPFA_Inpainting.m: The BPFA image inpainting program. Subprograms for Gibbs sampling: SampleDZS.m: Sampling the dictionary D, the binary indicating matrix Z, and the pseudo weight matrix S. Used for no missing data case. SampleDSZ_MissingData: Sampling the dictionary D, the binary indicating matrix Z, and the pseudo weight matrix S. Used when there are missing data. 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 XUpdate.m: Update the training data set in sequential learning. Used for no missing data case. XUpdate_MissingData.m: Update the training data set in sequential learning. Used when there are missing data. SZUpdate.m: Update the pseudo weight matrix S and binary indicating matrix Z in sequential learning. idexUpdate.m: Update the index of the training data in the input data matrix X. YfalgUpdate.m: Update the binary mask matrix Yflag associated with the input data matrix X. Other subprograms: sparsity.m: Squeeze out zero components in the sparse matrix. DispDictionary.m: Display the dictionary elements as a image DenoiseOutput.m: Reconstruct the image InitMatrix.m: Initialization

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