A new approach has been proposed to improve the computational performance of denoising in which adaptively defined learning step size has been used for tuning the parameter of the thresholding function of wavelet transform-based thresholding neural network (WT-TNN) methodology. In this approach, steepest gradient-based learning step size of WT-TNN methodology are changed to the proposed adaptively defined learning step size for tuning the parameters of thresholding function. The results of the image enhanced by such adaptive learning step size exhibit the increase in the speed of learning and improved edge preservation feature. Further, the learning time has also become independent of noise level and initial values of learning parameters.
Inspec keywords: edge detection; image denoising; neural nets; telecommunication computing; image enhancement
Other keywords:
Subjects: Optical, image and video signal processing; Computer vision and image processing techniques; Neural computing techniques
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http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2010.0014
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