An Edge-Preserving Image Reconstruction Using Neural Network
Journal of Mathematical Imaging and Vision
A Local-Information-Based Blind Image Restoration Algorithm Using a MLP
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Image compression by vector quantization with recurrent discrete networks
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
| Hi-index | 0.01 |
The performance of a classical linear vector predictor is limited by its ability to exploit only the linear correlation between the blocks. However, a nonlinear predictor exploits the higher order correlations among the neighboring blocks, and can predict edge blocks with increased accuracy. We have investigated several neural network architectures that can be used to implement a nonlinear vector predictor, including the multilayer perceptron (MLP), the functional link (FL) network, and the radial basis function (RBF) network. Our experimental results show that a neural network predictor can predict the blocks containing edges with a higher accuracy than a linear predictor