What size net gives valid generalization?
Neural Computation
Neural approach for TV image compression using a hopefield type network
Advances in neural information processing systems 1
Wavelets and subband coding
Finite automata based compression of bi-level images
DCC '96 Proceedings of the Conference on Data Compression
Finite Automata and Regularized Edge Preserving Wavelet Transform Scheme
DCC '99 Proceedings of the Conference on Data Compression
Nonlinear vector prediction using feed-forward neural networks
IEEE Transactions on Image Processing
Variational approach for edge-preserving regularization using coupled PDEs
IEEE Transactions on Image Processing
A new, fast, and efficient image codec based on set partitioning in hierarchical trees
IEEE Transactions on Circuits and Systems for Video Technology
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This paper presents an image restoration model based on the implicit function theorem and edge-preserving regularization. We then apply the model on the subband-coded images using the artificial neural network. The edge information is extracted from the source image as a priori nowledge to recover the details and reduce the ringing artifact of the subband-coded image. The multilayer perceptron model is employed to implement the restoration process. The main merit of the presented approach is that the neural network model is massively parallel with strong robustness for the transmission noise and parameter or structure perturbation, and it can be realized by VLSI technologies for real-time applications. To evaluate the performance of the proposed approach, a comparative study with the set partitioning in hierarchical tree (SPIHT) has been made by using a set of gray-scale digital images. The experimental results showed that the proposed approach could result in compatible performances compared with SPIHT on both objective and subjective quality for lower compression ratio subband coded image.