Neuralizing target superresolution algorithms

IEEE Geoscience and Remote Sensing Letters

Volumn 1, Issue 4, 2004, Pages 318-321

  Collins, Michael ^a   De Jong, Michael ^b  

^a UNIVERSITY OF CALGARY   (Canada)

^b UNIVERSITY OF WESTERN ONTARIO   (Canada)

Author keywords

Hopfield neual net; Sigmoid switch; Superresolution

Indexed keywords

ALGORITHMS; CONSTRAINT THEORY; FUNCTIONS; ITERATIVE METHODS; NEURAL NETWORKS; OPTICAL RESOLVING POWER; OPTIMIZATION; REMOTE SENSING;

HOPFIELD NEURAL NET; SIGMOID FUNCTIONS; SIGMOID SWITCH; SUPERRESOLUTION;

IMAGING TECHNIQUES;

EID: 8344263049     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2004.836258     Document Type: Article

References (3)

1. R. Malladi and J. A. Sethian, "Image processing! Flows under min/max curvature and mean curvature," Graph. Models Image Process., vol. 58, no. 2, pp. 127-141, 1996.
2. A. Tatem, N. G. Lewis, P. M. Atkinson, and M. S. Nixon, "Super-resolution target identification from remotely sensed images using a Hop-field neural network," IEEE Trans. Geosci. Remote Sensing, vol. 39, pp. 781-796, Apr. 2001.
3. J. J. Hopfield, "The neurons with graded response have collective computational properties like those of two-state neurons," Proc. Nat. Acad. Sci., vol. 81, pp. 3088-3092, 1984.