NOISE TYPE IDENTIFICATION USING MACHINE LEARNING Sohail Masood 2,4 , Ayyaz Hussain 1 , and M. Arfan Jaffar 2,3 1 Department of Computer Science, International Islamic University Islamabad, Pakistan 2 Department of Computer Science, National University of Computer and Emerging Sciences, FAST-NU, Islamabad, Pakistan 3 Gwangju Institute of Science & Technology, Gwangju, South Korea 4 Center for Advanced Research in Engineering (CARE), Islamabad, Pakistan rsmbhatti@gmail.com, ayyaz.hussain@iiu.edu.pk, arfan.jaffar@nu.edu.pk, ABSTRACT In this paper, we have proposed a new technique for automatically identifying the type of noise in digital images. Our machine learning based noise Type identification scheme uses some well-known statistical parameters to distinguish different types of noises. Local features of 3x3 window are used to train the machine learning based classifier. We have catered for 2 types of noise (salt & peppers and random-valued) in this paper. Experiments show that the proposed technique gives promising results and can be enhanced to be a generic noise identification system for every type of noise. KEY WORDS Noise Type Identification, Machine Learning, Digital Image Processing 1. Introduction Noise detection and removal from digital images is very primary task in most of digital image processing applications. Images corrupted with noise are first analyzed to find the type of noise and then specific noise detection and removal algorithm is applied for that type of noise. Different applications assume the type of expected noise depending upon their environment and apply algorithm for detection and removal of that noise type. With the increase in digital image processing applications for versatile and dynamic environment, the assumption of a specific type of noise is no more valid. Now a days, image processing applications are used in variety of way and in almost every discipline of life. So images can get corrupted with different types of noise in different times in a dynamic environment. There is a need of some mechanism to automatically identify the type of noise; so that one can apply specific algorithm for that type of noise. Noise type identification is a very new topic of research and very little work has been done on it. In [1], Noise identification using Local Histograms method is proposed which consists of roughly segmenting and labeling the noisy image. The image of labels is then used for the selection of homogeneous regions. In [2], a neural network based technique for identifying the type of noise present in a noisy image is proposed. The proposed method exhibits fast training process and does not require any assumption in the given images such as homogeneous areas etc. Its accuracy gets down with the increase in noise density. [3, 4, 5] implement statistical feature extraction for calculating the statistical properties and a simple pattern classification scheme is applied on the features to identify the noise type present in an image. This method first applies noise removal filters for all types of noises, subtracts the resulting image from original image to get noise and then tries to identify it. In [[14]], Gonzalez and woods has given some methods for noise type identification, which are based on histogram analysis. These methods are based on global perspective and can be used to get an estimate about occurrence of a noise type in an image. These methods have some limitations or assumptions to work e.g. they require imaging system to be present or location information of noise is known or a single type of noise present in the image [[14]]. All the techniques available in literature simply inform about presence of a certain type of noise in an image but can’t tell about the location of the noise. In this paper we have proposed a generic noise type identification method, which identifies noise type based of local window and thus can tell about the noise type in each corrupted pixel individually. Our technique not only works good for whole range of noise but also performs very well for mixed noise. DOI: 10 12792/icisip2014.022 106 Proceedings of the 2nd International Conference onIntelligent Systems and Image Processing 2014 © 2014The Institute of Industrial Applications Engineers, Japan.