| 摘要 |
A signal processing system for a video camera uses a single neural network to implement multiple nonlinear signal processing functions. In one example, the neural network implements gamma correction, contrast compression, color correction, high pass filtering and aperture correction as a combined function which is emulated by the network. The network is trained off-line using back propagation to emulate the entire composite function for a set of parameters which results in multiple sets of weighting factors. Then, using the stored multiple sets of weighting factors as initial values, the neural network is "re-trained" on-line for each new parameter setting. The use of a single neural network in place of the multiple dedicated processing functions reduces engineering effort to develop the product and may reduce the cost of the total system.
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