Abstract
In an image restoration problem we usually have two different kinds of information. In the first stage, we have knowledge about the structural form of the noise and local characteristics of the image. These noise and image models normally depend on unknown hyperparameters. The hierarchical Bayesian approach adds a second stage by putting a hyperprior on these hyperparameters, through which information about these hyperparameters is included. In this work we relate the hierarchical Bayesian approach to image restoration to an iterative approach for estimating these hyperparameters in a deterministic way.
| Original language | English (US) |
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| Title of host publication | Proceedings of SPIE - The International Society for Optical Engineering |
| Pages | 244-251 |
| Number of pages | 8 |
| Volume | 2308 |
| Edition | p 1 |
| State | Published - Dec 1 1994 |
| Event | Visual Communications and Image Processing '94 - Chicago, IL, USA Duration: Sep 25 1994 → Sep 29 1994 |
Other
| Other | Visual Communications and Image Processing '94 |
|---|---|
| City | Chicago, IL, USA |
| Period | 9/25/94 → 9/29/94 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Condensed Matter Physics