تقرير
CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration
| العنوان: | CLEAR: Covariant LEAst-square Re-fitting with applications to image restoration |
|---|---|
| المؤلفون: | Deledalle, C-A., Papadakis, N., Salmon, J., Vaiter, S. |
| سنة النشر: | 2016 |
| المجموعة: | Computer Science Mathematics Statistics |
| مصطلحات موضوعية: | Mathematics - Statistics Theory, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning |
| الوصف: | In this paper, we propose a new framework to remove parts of the systematic errors affecting popular restoration algorithms, with a special focus for image processing tasks. Generalizing ideas that emerged for $\ell_1$ regularization, we develop an approach re-fitting the results of standard methods towards the input data. Total variation regularizations and non-local means are special cases of interest. We identify important covariant information that should be preserved by the re-fitting method, and emphasize the importance of preserving the Jacobian (w.r.t. the observed signal) of the original estimator. Then, we provide an approach that has a "twicing" flavor and allows re-fitting the restored signal by adding back a local affine transformation of the residual term. We illustrate the benefits of our method on numerical simulations for image restoration tasks. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1606.05158 |
| رقم الأكسشن: | edsarx.1606.05158 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |