Abstract
In this correspondence, we consider the use of nonlinear estimators for the noise smoothing of images obtained under quantum-limited imaging conditions. A Volterra expansion is investigated from which a set of linear-quadratic filters is derived using higher order statistics. The filters are applicable for single frame and multiple frames of a single scene imaged under low-light levels.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1328-1333 |
| Number of pages | 6 |
| Journal | IEEE Transactions on Image Processing |
| Volume | 4 |
| Issue number | 9 |
| DOIs | |
| State | Published - Sep 1995 |
Funding
Various approaches for postprocessing of images degraded by quantum noise have been proposed in the past. Because this type of noise is typically placed into the class of signal-dependent degradations, the majority of these techniques lie in the specification of a nonstationary model for the description of the noise [4], [5], resulting in a genre of nonstationary filters that operate independently at each pixel. This is intuitive with the notion of signal-dependent noise; however, these techniques also have a common feature in that they are linear. PTde it is well known that a nonlinear filter will generally Manuscript received August 18, 1993; revised October 28, 1994. This work was supported in part by a grant from Siemens. The associate editor coordinating the review of this paper and approving it for publication was Dr. Hsueh-Ming Hang.
ASJC Scopus subject areas
- Software
- Computer Graphics and Computer-Aided Design