Home > Published Issues > 2013 > Volume 1, No. 1, March 2013 >

Image Reconstruction and Edge Detection based upon Neural Approximation Characteristics

Manoj Kumar Singh
Manuro Tech Research, Bangalore, India

Abstract—In image processing based applications there are very important requirements of noise removal and edge detection. In this paper universal approximation characteristic of feedforward neural is taken to achieve both of these requirements in a simple but very efficient way. Concept of local pattern generated by small region pixels which define the possibility of relation among pixels is presented. This approach facilitates the solution as universal solution for different types of noise removal compare to conventional solutions which are based on noise characteristics. Same model of neural network with little extension can also be utilized as edge detector has also presented. Another benefit of proposed model is contrast enhancement without any extra computation cost. In effect this solution can be considered as universal solution for noise reduction, edge detection and contrast enhancement. Comparison has made with well established solution like median filter and adaptive Wiener filter for noise reduction where as Canny and Prewitt detectors have taken for edge detection comparison.

Index Terms—noise reduction, edge detection, contrast enhancement, neural network, universal approximation.

Cite: Manoj Kumar Singh, "Image Reconstruction and Edge Detection based upon Neural Approximation Characteristics," Journal of Image and Graphics, Vol. 1, No. 1, pp. 12-16, March 2013. doi: 10.12720/joig.1.1.12-16