Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Digital Image Restoration
Finite automata based compression of bi-level images
DCC '96 Proceedings of the Conference on Data Compression
The Application of Constrained Least Squares Estimation to Image Restoration by Digital Computer
IEEE Transactions on Computers
General choice of the regularization functional in regularized image restoration
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
Weight assignment for adaptive image restoration by neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A software engineering approach to develop adaptive RBF neural networks
Design and application of hybrid intelligent systems
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Image restoration is an important issue in image processing, which helps recovering of degraded images caused by various factors in different circumstances. Neural networks models have been successfully applied in handling image restoration problems and some progress and promising results have been reported in the past. A popular neural networks model for image restoration is the Hopfield network due to its ability on dealing with optimization problems. A degraded image may have multiple corresponding solutions, i.e., the restored images, and the obtained solution can be sub-optimal which is related to a local minimum point in the weight space of the Hopfield network. This paper gives an algebraic characterization on images and shows that images with lower complexity can be restored uniquely from their degraded images and edge information. The obtained result in this paper establishes a mathematical basis for employing the mapping neural networks to realize a learning based image restoration scheme. The linear image restoration model is firstly generalized to a nonlinear one to broaden the scope of application. Secondly, we view the image restoration task as a set of approximation problems in a high dimensional space, and a mapping relationship between the degraded images with edge information and the source images is then built using feed-forward neural networks and the well trained neural network can be used to restore the degraded images in realtime. Computer simulations demonstrate the effectiveness of the learning image restoration techniques proposed in this paper.