From Patches to Images: A Nonparametric Generative Model
Authors: Geng Ji, Michael C. Hughes, Erik B. Sudderth
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our denoising performance on standard benchmarks is superior to EPLL and comparable to the state-of-the-art, and we provide novel statistical justifications for common image processing heuristics. We also show accurate image inpainting results. |
| Researcher Affiliation | Academia | 1Brown University, Providence, RI, USA. 2Harvard University, Cambridge, MA, USA. 3University of California, Irvine, CA, USA. |
| Pseudocode | No | The paper describes methods and processes but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks or figures. |
| Open Source Code | Yes | Our open-source Python code is available online at github.com/bnpy/hdp-grid-image-restoration. |
| Open Datasets | Yes | Following EPLL, we train our HDP-Grid model using 400 clean training and validation images from the Berkeley segmentation dataset (BSDS, Martin et al. (2001)). |
| Dataset Splits | No | Following EPLL, we train our HDP-Grid model using 400 clean training and validation images from the Berkeley segmentation dataset (BSDS, Martin et al. (2001)). |
| Hardware Specification | No | To denoise a 512 512 pixel image on a modern laptop, our Python code for e DP inference with K = 449 clusters takes about 12 min. |
| Software Dependencies | No | Our open-source Python code is available online at github.com/bnpy/hdp-grid-image-restoration. |
| Experiment Setup | Yes | We fix δ = 0.5/255 to account for the quantization of image intensities to 8-bit integers. We initialize inference by creating K = 100 image-specific clusters with the k-means++ algorithm (Arthur & Vassilvitskii, 2007)... and refine with 50 iterations of coordinate descent updates... We set our annealing schedule for κ to match that used by the public EPLL code. |