Computational Research Progress in Applied Science & Engineering ©PEARL publication, 2017 CRPASE Vol. 03(04), 132-135, December 2017 ISSN 2423-4591 Survey of Image De-noising using Wavelet Transform Combined with Thresholding Functions Noorbakhsh Amiri Golilarz a , Niyifasha Robert b , Jalil Addeh c a Department of Electrical and Electronic Engineering, Eastern Mediterranean University, Cyprus b Center for Cyber Security, School of Computer Science and Engineering, University of Electronic Science and Technology of China, China c Department of Electrical Engineering, Babol Noshirvani University of Technology, Babol, Iran Keywords Abstract Noise reduction, Thresholding function, Image de-noising, Wavelet transform. Noise reduction is still a challenging problem for researchers. Many algorithms have been published in this subject and each finding has its own benefit and restriction. In this paper, a review of noise removing using some unique thresholding functions is conducted. All of these techniques used wavelet transform combined with thresholding functions for image de-noising. Their proposed thresholding functions have some properties like nonlinearity, continuity and smoothness. These functions were introduced to overcome the standard soft and hard thresholding functions and also to improve the performance analysis and enhance the visual quality of the images in terms of obtaining higher peak signal to noise ratio. Therefore, we can call these functions as improved version of standard soft and hard thresholding functions. 1. Introduction Digital images play a significant role in daily events and also in areas of research and technology. Imperfect devices, troublesome during capturing, receiving and transmitting processes and meddling natural phenomena all degrade the data of interest [1]. Thus, analyzing the images may not be possible until we properly discard the noise from the images. It is necessary to apply efficient image de-noising techniques to remove the noise and enhance the visual quality of image. Nowadays, wavelet based image de-noising has become very popular among the researchers investigating the image processing. Donoho and Johnstone proposed ideal spatial adaption by wavelet shrinkage in 1993 [2] and adaptive to unknown smoothness via wavelet shrinkage in 1995 [3]. Norouzzadeh and Rashidi suggested a new thresholding function in wavelet domain for image de-noising [4]. Chang et al. [5] proposed the adaptive wavelet thresholding for image de-noising and compression. Dong in 2013 introduced adaptive image de-noising using wavelet thresholding [6]. Anisimova et al. used the efficiency of wavelet coefficients thresholding techniques for multimedia and astronomical image de-noising [7]. Chen and Qian in 2011 [8] introduced de-noising of hyper-spectral imagery using principal component analysis and wavelet shrinkage. A transformation for ordering multispectral data in terms of  Corresponding Author: E-mail address: noorbakhsh.amiri@students.emu.edu.tr Received: 09 September 2017; Accepted: 20 November 2017 image quality with implications for noise removal is proposed by Green et al. [9]. Thresholding neural network for adaptive noise reduction is introduced in a study conducted by Zhang [10]. Zhang and Desai used adaptive de- noising based on SURE risk [11]. Image de-noising in the wavelet domain using a new adaptive thresholding function has been introduced by Nasri and Nezamabadi-pour [12]. Guo et al. [13] used an efficient SVD-based method for image de-noising. Amiri Golilarz et al. [14] introduced the translation invariant wavelet based noise reduction using a new smooth nonlinear improved thresholding function. Wavelet image de-noising based on improved thresholding neural network and cycle spinning was proposed by Sahraeian et al. [15]. In addition, Amiri Golilarz and Demirel proposed thresholding neural network (TNN) based noise reduction with a new improved thresholding function [16]. Noise suppression using wavelet transform (WT) requires applying thresholding function with a suitable threshold value to keep the large coefficients (most important features) of the image and remove small noisy components. In this article, we presented a survey of some unique techniques for image de-noising. These methods were proposed to remove the noise from the images using WT combined with different thresholding functions.