TY  - JOUR
T1  - Wavelet Coefficient Fusion Method -Based Image Denoising
AU - Ali, Israa Hadi AU - Al_taie, Russell H. 
JO  - Research Journal of Applied Sciences
VL  - 11
IS  - 10
SP  - 1045
EP  - 1049
PY  - 2016
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2016.1045.1049
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2016.1045.1049
KW  - Discrete wavelet transform
KW  -noise image
KW  -denoising wavelet
KW  -image fusion
KW  -level decomposition
AB  - In this study, new manner for removing noise from image using wavelet fusion method. The main aim of this research is restaurant the image based on peak signal to noise ratio measure. The key idea is compared each sub band for different levels of wavelet based on PSNR value. Initially apply discreet wavelet transform with 2level decomposition on the set of images .Then perform denoising wavelet techniques that achieved by threshold value for detail coefficient and compare it with wavelet coefficients for detail sub band. After that select sub band that has less noise from each image, sub band that contain high PSNR measure is the optimal. Finally apply IDWT process to convert the result image from frequency domain to spatial domain. The outcomes of the work exposed that the number of levels increases, PSNR of image decrease. In this study was chosen two level of decomposition to guarantee choosing several sub band for fusion process but the increasing in number of levels of wavelet will lose the essential information of image, therefore level 1 is better than level 2.
ER  -