Multi Focus Image Fusion Using Conversion Neural Network - Han, Xiao
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In real life, the effect of taking pictures is some times unsatisfactory, so it is necessities to fuse the distinct regions of different focus targets in the same scene to obtain the global clear image, which is called multi focus image fusion. The traditional image fusion algorithm has the problems of complex model design and poor fusion image quality, while the dee P learning technology uses neural network for image feature learning, which has fast training and strong functionality. Before, this paper uses conventional neural network to optimize the problems existing in the multi focus image fusion algorithm.…mehr

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Produktbeschreibung
In real life, the effect of taking pictures is some times unsatisfactory, so it is necessities to fuse the distinct regions of different focus targets in the same scene to obtain the global clear image, which is called multi focus image fusion. The traditional image fusion algorithm has the problems of complex model design and poor fusion image quality, while the dee P learning technology uses neural network for image feature learning, which has fast training and strong functionality. Before, this paper uses conventional neural network to optimize the problems existing in the multi focus image fusion algorithm.
Autorenporträt
Dr. Xiao Han, School of computer science, Jiangsu University of Science and Technology