Name
Abstract | ||
|---|---|---|
Images can be represented as the composition of multiple intrinsic component images, such as shading, albedo, and noise images. In this paper, we present a method for estimating intrinsic component images from a single image, which we apply to the problems of estimating shading and albedo images and image denoising. Our method is based on learning estimators that predict filtered versions of the desired image. Unlike previous approaches, our method does not require unnatural discretizations of the problem. We also demonstrate how to learn a weighting function that properly weights the local estimates when constructing the estimated image. For shading estimation, we introduce a new training set of real-world images. The accuracy of our method is measured both qualitatively and quantitatively, showing better performance on the shading/albedo separation problem than previous approaches. The performance on denoising is competitive with the current state of the art. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/CVPR.2006.114 | CVPR |
Field | DocType | Volume |
Noise reduction,Computer vision,Pattern recognition,Computer science,Albedo,Nonlinear regression,Artificial intelligence,Image denoising,A-weighting,Instrumental and intrinsic value,Estimator,Shading | Conference | 2 |
ISBN | Citations | PageRank |
0-7695-2597-0 | 35 | 2.53 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marshall F. Tappen | 1 | 1901 | 89.34 |
| Edward H. Adelson | 2 | 1768 | 320.52 |
| William T. Freeman | 3 | 17382 | 1968.76 |