Theoretical Foundation for Nonlinear Edge-Preserving Regularized Learning Image Restoration
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Two image restoration algorithms using variational PDE based neural network
IWCMC '07 Proceedings of the 2007 international conference on Wireless communications and mobile computing
The optimal design of weighted order statistics filters by using support vector machines
EURASIP Journal on Applied Signal Processing
On Non-Uniform Rational B-Splines Surface Neural Networks
Neural Processing Letters
Semi-blind image restoration using a local neural approach
Neurocomputing
Model selection criteria for image restoration
IEEE Transactions on Neural Networks
Semi-blind image restoration using a local neural approach
SPPRA '08 Proceedings of the Fifth IASTED International Conference on Signal Processing, Pattern Recognition and Applications
An MLP neural net with L1 and L2 regularizers for real conditions of deblurring
EURASIP Journal on Advances in Signal Processing
An edge preserving regularization model for image restoration based on hopfield neural network
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Estimation and optimization based ill-posed inverse restoration using fuzzy logic
Multimedia Tools and Applications
Hi-index | 0.00 |
This paper presents a scheme for adaptively training the weights, in terms of varying the regularization parameter, in a neural network for the restoration of digital images. The flexibility of neural-network-based image restoration algorithms easily allow the variation of restoration parameters such as blur statistics and regularization value spatially and temporally within the image. This paper focuses on spatial variation of the regularization parameter. We first show that the previously proposed neural-network method based on gradient descent can only find suboptimal solutions, and then introduce a regional processing approach based on local statistics. A method is presented to vary the regularization parameter spatially. This method is applied to a number of images degraded by various levels of noise, and the results are examined. The method is also applied to an image degraded by spatially variant blur. In all cases, the proposed method provides visually satisfactory results in an efficient way