Neural networks for local recognition of images with mixed noise
Alexander N. Dolia, Владимир Васильевич Лукин, Alexander A. Zelensky, Jaakko T. Astola, Chris Anagnostopoulos · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
A new approach to neural network (NN) application for local recognition of images with mixed noise is put forward. Although some pixels in images can be corrupted by spikes the proposed technique permits to eliminate uncertainty observed in this case and to correctly recognize the pixels that, in fact, correspond to an edge, a homogeneous region or an small-sized object. For this purpose a procedure of recognition of one among four basic hypotheses and additional determination of spike properties within the scanning window is proposed. This recognition task is performed for two groups of outputs (classes) of one common NN. The problems of NN learning and structure selection for this case are discussed. The performance of neural network classifier is analyzed for different input data types and for various characteristics of noise. An improvement in correct recognition is shown for the proposed approach in comparison to previous work.