Image Denoising --- The SURE-LET Methodology
Event details
| Date | 24.06.2011 |
| Hour | 14:15 |
| Speaker | Prof. Thierry Blu, The Chinese University of Hong Kong |
| Location | |
| Category | Conferences - Seminars |
The goal of this presentation is to promote a new approach for dealing with noisy data --- typically, images or videos here. Image denoising consists in approximating the noiseless image by performing some, usually non-linear, processing of the noisy image. Most standard techniques involve assumptions on the result of this processing (sparsity, low high-frequency contents, etc.); i.e., the denoised image. Instead, the SURE-LET methodology consists in approximating the processing itself (seen as a function) over some linear combination of elementary non-linear processings (LET: Linear Expansion of Thresholds), and to optimize the coefficients of this combination by minimizing a statistically unbiased estimate of the Mean Square Error (SURE: Stein's Unbiased Risk Estimate, in the case of additive Gaussian noise). I will introduce the technique and outline its advantages (fast, noise-robust, flexible, image adaptive). A comprehensive set of results will be shown and compared with the state-of-the-art. Prof. Blu's homepage
Practical information
- General public
- Free