Authors: A. Prathik; J. Anuradha; K. Uma
Addresses: School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India ' School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India ' School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, India
Abstract: This paper proposed a hybrid wavelet double window median filter (HWDWM) which is made by blending decision-based coupled window median filter and discrete wavelet transform (DWT) and review is made to increase the filters which are widespread for removing noise. In the proposed filter there are double window such as row window and column window. This proposed method takes the noisy image for processing and it moves row window for indexing from the 1st pixel of the noisy image up to the last pixel of the noised image then indexing is made by column window then decompose the signal of the image to provide the localisation. The noisy image is decomposed by DWT and then coefficients are transformed to independent distributed variables. The coefficients are then analysed on the basis of thresholding. The image is reconstructed using wavelet transform's inverse after the threshold. Experiments were executed in order to show the effect of noise removal filters on soil image. Two metrics are used to measure the quality of image; they are: peak signal to noise ratio (PSNR) and root mean square error (RMSE). Experimental results show the superiority of this filter over other noise removal filters.
Keywords: data mining; soil classifications; filters; peak signal to noise ratio; PSNR; mean square error; MSE.
International Journal of Cloud Computing, 2022 Vol.11 No.1, pp.14 - 26
Received: 15 Jul 2019
Accepted: 13 Sep 2019
Published online: 18 Feb 2022 *