Abstract
This paper presents a retrospective algorithm for correcting the uneven illumination field in microscopy images. The illumination field is iteratively made uniform using an increasing sequence of bivariate polynomials. At each iteration, the least squares problem of fitting a 2-D polynomial to a sampled image is solved by using QR decomposition with column pivoting, where image samples are obtained by dynamic programming or watershed transform. This incremental scheme allows the smoothness constraint of the estimated bias field to be implicitly satisfied. The proper number of iterations is determined by an automatic stopping criterion. The experimental results show the effectiveness of the proposed approach when compared to a set of different well-established methods.
Original language | English |
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Pages (from-to) | 967-974 |
Number of pages | 8 |
Journal | Signal, Image and Video Processing |
Volume | 10 |
Issue number | 5 |
Early online date | 11 Dec 2015 |
DOIs | |
Publication status | Published - 31 Jul 2016 |
Externally published | Yes |
Keywords
- Dynamic programming
- Illumination correction
- Image restoration
- Image sampling
- Least squares
- Linear inverse problems
- Surface fitting