Name
Abstract | ||
|---|---|---|
Effective image prior is a key factor for successful image denoising. Existing learning-based priors require a large collection of images for training. Besides being computationally expensive, these training images do not necessarily correspond to the noisy image of interest. In this paper, we propose an adaptive learning procedure for learning image patch priors. The new algorithm, called the Expectation-Maximization (EM) adaptation, maps a generic prior to a targeted image to create a specific prior. EM adaptation requires significantly less amount of training data compared to the standard EM, and can be applied to pre-filtered images in the absence of clean databases. Experimental results show that the adapted prior is consistently better than the originally un-adapted prior, and has superior performance than some state-of-the-art algorithms. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP) | Image denoising, Gaussian Mixture models, Expectation-Maximization, Expected Patch Log-Likelihood, EM-adaptation, BM3D |
Field | DocType | Citations |
Noise reduction,Computer vision,Information processing,Noise measurement,Pattern recognition,Computer science,Non-local means,Image denoising,Artificial intelligence,Prior probability,Video denoising,Adaptive learning | Conference | 2 |
PageRank | References | Authors |
0.36 | 22 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Stanley H. Chan | 1 | 403 | 30.95 |
| Enming Luo | 2 | 84 | 6.63 |
| Truong Q. Nguyen | 3 | 1402 | 136.69 |