Journal of Engineering Science and Technology Review 8 (5) (2015) 41-48 Review Article A Review of Image Denoising Methods I. Irum*, M. A. Shahid, M. Sharif and M. Raza COMSATS Institute of Information Technology Wah Cantt, Pakistan Received 10 August 2015; Accepted 19 December 2015 ___________________________________________________________________________________________ Abstract Image Denoising is one of the fundamental and very important necessary processes in image processing. It is still a challenging and a hot problem for researchers. Images are one of essential representations in every field like education, agriculture, geosciences, aerospace, surveillance, entertainment etc by means of electronic or print media. Images can get corrupted by noise, there has been a great research effort which made solutions for this problem, a number of methods have been proposed. An overview of various methods is given here after a brief introduction. These methods have been categorized on the bases of techniques used. Keywords: Derivative Based Denoising, Fuzzy Based Denoising, Mathematical Morphology Based Denoising, Median Based Denoising, Nonlinear Denoising methods, s Statistical Modeling Based Denoising __________________________________________________________________________________________ 1. Introduction Various sources let the digital images to be corrupted by poor contrast and noise, theses sources include image transmission, acquisition, compression [1-5], quantization, illumination conditions [6], malfunctioning instruments, ill positions etc. These sources directly degrade the visual quality of image during processing of image [7]. Process of image denoising or image restoration is targeted to estimate the original image from corrupted image. It is still the most fundamental, largely unsolved and widely studied problem [8]. Image has important information and certain details, as communication through visual images is an integral part of modern life. Images and videos are used everywhere to communicate as a visual source. Noise affects image features seriously like edges, thin lines, and loss of image details provides degradation of spatial resolutions. Therefore noise removal from corrupted images is very important and necessary before further processing on them like segmentation [9-12], feature matching, edge detection, feature extraction [13-15], feature detection [16] of image details used for face recognition [17-38] etc, These denoised images can be used for face detection [39-41], content based image retrieval [42-48], medical image reconstruction [49], understanding Morphology of medical images [50] with its applications [51], medical image enhancement [52], image rendering [53] etc. A huge number of methods have been proposed in this context over the past decades in image processing. A review of these methods has been given here covering the representative methods which gave better performance in this area. These methods have been categorized on the bases of their natures of techniques used in. These categories are Median Based, Statistical Modeling Based, Derivative Based, Fuzzy Logic Based and Mathematical Morphology Based image denoising methods. These categories are discussed one by one in upcoming section of rest of the paper and conclusion is given at the end. 2. Median Based Image Denoising Methods Median Based Filters or Denoising Methods are the corner stones of image cancellation methods in modern image processing. Tukey [54] first introduced the Median Filter, after its inception, tremendous efforts have been made for optimization, improvement and refinement over the years. Standard median filters presented in [55] treated all the pixels of the image whether corrupted or uncorrupted. [56] To overcome this drawback Weighted Median Filters (WMF) [57] and Switching Median Filter (SMF) [58] were proposed. WMF reduced the smoothing effect, preserved the image sharpness and treated the entire pixels like standard median filter by giving higher weights to the central pixel [59-60]. Weighted Order Statistics Median Filters (WOSFs) were proposed in [61]. Design of WMF admitting negative and complex weights presented in [62-65]. Steerability concept was introduced in [66] and its application found in [67]. Recently Dimitrios Charalampidis [68] proposed steerable WMF inheriting the noise robustness and edge preserving capabilities of WMF. SMF reduced the number of pixels subjected to filtration by identifying the corrupted and uncorrupted pixels and leaving the uncorrupted pixels unchanged. In [69-80] extensions of median filter and SMF have been presented. Recent examples of noise adaptive approaches can be found in [81-83]. The parameters that can be used for as input function to adaptive approach can be window size, shape or rank [84]. It created blurred images when applied to mix or Gaussian Noise, Directional Weighted Median (DWM) [85] Jestr JOURNAL OF Engineering Science and Technology Review www.jestr.org ____________ * E-mail address: ismairum@gmail.com ISSN: 1791-2377 © 2015 Kavala Institute of Technology. All rights reserved.