| Paper: | WP-L1.4 |
| Session: | Soft Computing in Image Processing: Recent Advances |
| Time: | Wednesday, September 19, 15:30 - 15:50 |
| Presentation: |
Special Session Lecture
|
| Title: |
A MACHINE LEARNING FRAMEWORK FOR ADAPTIVE COMBINATION OF SIGNAL DENOISING METHODS |
| Authors: |
David Hammond; New York University | | |
| | Eero Simoncelli; New York University | | |
| Abstract: |
We present a general framework for combination of two distinct local denoising methods. Interpolation between the two methods is controlled by a spatially varying decision function. Assuming the availability of a clean training data, we formulate a learning problem for determining the decision function. As an example application we use Weighted Kernel Ridge Regression to solve this learning problem for a pair of wavelet-based image denoising algorithms, yielding a ``hybrid'' denoising algorithm whose performance surpasses that of either initial method. |
