TY  - CONF
AU  - Laura Lopez-Fuentes
AU  - Sebastia Massanet
AU  - Manuel Gonzalez-Hidalgo
A2  - FUZZ-IEEE
PY  - 2017//
TI  - Image vignetting reduction via a maximization of fuzzy entropy
BT  - IEEE International Conference on Fuzzy Systems
N2  - In many computer vision applications, vignetting is an undesirable effect which must be removed in a pre-processing step. Recently, an algorithm for image vignetting correction has been presented by means of a minimization of log-intensity entropy. This method relies on an increase of the entropy of the image when it is affected with vignetting. In this paper, we propose a novel algorithm to reduce image vignetting via a maximization of the fuzzy entropy of the image. Fuzzy entropy quantifies the fuzziness degree of a fuzzy set and its value is also modified by the presence of vignetting. The experimental results show that this novel algorithm outperforms in most cases the algorithm based on the minimization of log-intensity entropy both from the qualitative and the quantitative point of view.
UR  - http://dx.doi.org/10.1109/FUZZ-IEEE.2017.8015706
N1  - LAMP; 600.120
ID  - Laura Lopez-Fuentes2017
ER  -