1 April 1998 Coupled lattice neural network for blind deconvolution
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In the research, we introduced an artificial neural network model named as coupled lattice neural network to reconstruct an original image from a degrade one in the blind deconvolution, where the original image and blurring function are not known. In the coupled lattice neural network, each neuron connects with its nearest neighbor neurons, the neighborhood corresponds to the weights of the neural network and is defined by a finite domain. Outputs of neurons shows the intensity distribution of an estimated original image. Weights of each neuron correspond to an estimated blur function and are the same for different neurons. The coupled lattice neural network includes two main operations, one is a nearest neighbor coupling or diffusion, the other is a local nonlinear reflection and learning. First a rule for a blur function growing is introduced. Then the coupled lattice neural network implements an estimated original image evolving based on an estimated blur function. Moreover we define a growing error criterion to control the evolution of the coupled lattice neural network. Whenever the error criterion is minimized, the coupled neural network gets stable, then outputs of the neural network correspond to the reconstructed original image, the weights are the blur function. In addition we demonstrate a method for the option of initial state variables of the coupled lattice neural network. The new approach to blind deconvolution can recover a digital binary image successful. Moreover the coupled lattice neural network can be used in the reconstruction of a gray-scale image.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ning Wang, Ning Wang, Yen-Wei Chen, Yen-Wei Chen, Zensho Nakao, Zensho Nakao, } "Coupled lattice neural network for blind deconvolution", Proc. SPIE 3307, Applications of Artificial Neural Networks in Image Processing III, (1 April 1998); doi: 10.1117/12.304647; https://doi.org/10.1117/12.304647


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