We design an image quality measure independent of local contrast changes, which constitute simple models of illumination changes. Given two images, the algorithm provides the image closest to the first one with the component tree of the second. This problem can be cast as a specific convex program called isotonic regression. We provide a few analytic properties of the solutions to this problem. We also design a tailored first order optimization procedure together with a full complexity analysis. The proposed method turns out to be practically more efficient and reliable than the best existing algorithms based on interior point methods. The algorithm has potential applications in change detection, color image processing or image fusion. A Matlab implementation is available at http://www.math.univ-toulouse.fr/ ∼ weiss/PageCodes.html.
Publié le : 2016-09-22
Classification:
Local contrast change,
topographic map,
isotonic regression,
convex optimization,
illumination invariance,
signal-to-noise-ratio,
image quality measure,
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV],
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV],
[MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC],
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
@article{hal-01370251,
author = {Weiss, Pierre and Escande, Paul and Dong, Yiqiu},
title = {Contrast Invariant SNR},
journal = {HAL},
volume = {2016},
number = {0},
year = {2016},
language = {en},
url = {http://dml.mathdoc.fr/item/hal-01370251}
}