A Speculative Method for Removal of Noise Using Iterative Median Filter
K. Sakthidasan Sankaran, V. Nagarajan · 2016
The denoising of a natural image corrupted by the Random valued desire noise is a classical problem in image processing. In this paper, we propose an efficient restoration method for the deduction of random-valued impulse noise. On the way to achieve the goal of better rebuilding, we employ a decision-tree- based impulse noise detector to identify the corrupted pixels and an edge-preserving sort out to rebuild the concentration values of corrupted pixels. To improve the quality of the digital images, a median based adaptive concept is used in reduction of random valued impulse noise. The results show that the proposed technique can achieve enhanced performances in terms of both quantitative estimate and image eminence than the prior methods. Furthermore, the performance can be enhanced by iterative median filter methods. For performance analysis the PSNR (dB) value resultant to original and reconstructed image is compared and edge preservation will be proved with edge detection methods.