Advance Implementation for Enhance Image using Neural Network and Optimal Image Processing Algorithm in Digital Image Processing

We review more than 200 applications of neural networks in image processing and discuss the present and possible future role of neural networks, especially feed-forward neural networks, Kohonen feature maps and Hop1eld neural networks. The various applications are categorized into a novel two-dimensional taxonomy for image processing al-gorithms. One dimension speci1es the type of task performed by the algorithm preprocessing, data reduction=feature extraction, segmentation, object recognition, image understanding and optimization. The other dimension captures the ion level of the input data processed by the algorithm pixel-level, local feature-level, structure-level, object-level, object-set-level and scene characterization. Each of the six types of tasks poses speci1c constraints to a neural-based approach. These speci1c conditions are discussed in detail. A synthesis is made of unresolved problems related to the application of pattern recognition techniques in image processing and speci1cally to the application of neural networks. Finally, we present an outlook into the future application of neural networks and relate them to novel developments.