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A variational approach to intensity approximation for remote sensing images using dynamic neural networks
Author(s) -
Zhou Shang Ming,
Li Hong Xing,
Xu Li Da
Publication year - 2003
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/1468-0394.00240
Subject(s) - regularization (linguistics) , computer science , pixel , discontinuity (linguistics) , artificial neural network , inverse problem , smoothness , image (mathematics) , artificial intelligence , algorithm , mathematics , mathematical analysis
Abstract: In remote sensing image processing, image approximation, or obtaining a high‐resolution image from a corresponding low‐resolution image, is an ill‐posed inverse problem. In this paper, the regularization method is used to convert the image approximation problem into a solvable variational problem. In regularization, the constraints on smoothness and discontinuity are considered, and the original ill‐posed problem is thereby converted to a well‐posed optimization problem. In order to solve the variational problem, a Hopfield‐type dynamic neural network is developed. This neural network possesses two states that describe the discrepancy between a pixel and adjacent pixels, the intensity evolution of a pixel and two kinds of corresponding weights. Based on the experiment in this study with a Landsat TM image free of added noise and a noisy image, the proposed approach provides better results than other methods. The comparison shows the feasibility of the proposed approach.