Hybrid Computational Intelligence Schemes in Complex Domains: An Extended Review
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
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
Artificial Neural Networks for Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid parallel projection approach to object-based image restoration
Pattern Recognition Letters
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
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
Semi-blind image restoration using a local neural approach
Neurocomputing
Model selection criteria for image restoration
IEEE Transactions on Neural Networks
An MLP neural net with L1 and L2 regularizers for real conditions of deblurring
EURASIP Journal on Advances in Signal Processing
An edge detection method by combining fuzzy logic and neural network
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Improving shape-based CBIR for natural image content using a modified GFD
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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We address the problem of adaptive regularization in image restoration by adopting a neural-network learning approach. Instead of explicitly specifying the local regularization parameter values, they are regarded as network weights which are then modified through the supply of appropriate training examples. The desired response of the network is in the form of a gray level value estimate of the current pixel using weighted order statistic (WOS) filter. However, instead of replacing the previous value with this estimate, this is used to modify the network weights, or equivalently, the regularization parameters such that the restored gray level value produced by the network is closer to this desired response. In this way, the single WOS estimation scheme can allow appropriate parameter values to emerge under different noise conditions, rather than requiring their explicit selection in each occasion. In addition, we also consider the separate regularization of edges and textures due to their different noise masking capabilities. This in turn requires discriminating between these two feature types. Due to the inability of conventional local variance measures to distinguish these two high variance features, we propose the new edge-texture characterization (ETC) measure which performs this discrimination based on a scalar value only. This is then incorporated into a fuzzified form of the previous neural network which determines the degree of membership of each high variance pixel in two fuzzy sets, the EDGE and TEXTURE fuzzy sets, from the local ETC value, and then evaluates the appropriate regularization parameter by appropriately combining these two membership function values