8 February 2015 Information theoretic methods for image processing algorithm optimization
Author Affiliations +
Abstract
Modern image processing pipelines (e.g., those used in digital cameras) are full of advanced, highly adaptive filters that often have a large number of tunable parameters (sometimes > 100). This makes the calibration procedure for these filters very complex, and the optimal results barely achievable in the manual calibration; thus an automated approach is a must. We will discuss an information theory based metric for evaluation of algorithm adaptive characteristics (“adaptivity criterion”) using noise reduction algorithms as an example. The method allows finding an “orthogonal decomposition” of the filter parameter space into the “filter adaptivity” and “filter strength” directions. This metric can be used as a cost function in automatic filter optimization. Since it is a measure of a physical “information restoration” rather than perceived image quality, it helps to reduce the set of the filter parameters to a smaller subset that is easier for a human operator to tune and achieve a better subjective image quality. With appropriate adjustments, the criterion can be used for assessment of the whole imaging system (sensor plus post-processing).
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sergey F. Prokushkin, Sergey F. Prokushkin, Erez Galil, Erez Galil, } "Information theoretic methods for image processing algorithm optimization", Proc. SPIE 9396, Image Quality and System Performance XII, 939604 (8 February 2015); doi: 10.1117/12.2083293; https://doi.org/10.1117/12.2083293
PROCEEDINGS
10 PAGES


SHARE
RELATED CONTENT


Back to Top