Gupta, Pradeep K and Kanhirodan, Rajan (2006) Design of a FIR filter for image restoration using principal component neural networks. In: IEEE International Conference on Industrial Technology, Dec 15-17, 2006, Bombay, India, pp. 1427-1432.
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The neural network finds its application in many image denoising applications because of its inherent characteristics such as nonlinear mapping and self-adaptiveness. The design of filters largely depends on the a-priori knowledge about the type of noise. Due to this, standard filters are application and image specific. Widely used filtering algorithms reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated general approach to design a finite impulse response filter based on principal component neural network (PCNN) is proposed in this study for image filtering, optimized in the sense of visual inspection and error metric. This algorithm exploits the inter-pixel correlation by iteratively updating the filter coefficients using PCNN. This algorithm performs optimal smoothing of the noisy image by preserving high and low frequency features. Evaluation results show that the proposed filter is robust under various noise distributions. Further, the number of unknown parameters is very few and most of these parameters are adaptively obtained from the processed image.
|Item Type:||Conference Paper|
|Additional Information:||Copyright 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.|
|Department/Centre:||Division of Physical & Mathematical Sciences > Instrumentation and Applied Physics (Formally ISU)|
|Date Deposited:||22 Oct 2010 04:21|
|Last Modified:||22 Oct 2010 04:21|
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