International Journal of Science and Research (IJSR) ISSN (Online): 2319-7064 Impact Factor (2012): 3.358 Volume 3 Issue 5, May 2014 www.ijsr.net Licensed Under Creative Commons Attribution CC BY A New Method for Noisy Image Segmentation using Firefly Algorithm Bhavana Vishwakarma 1 , Amit Yerpude 2 1 M. Tech Scholar, Department of Computer Science and Engg., CSVTU University Rungta College of Engineering & Technology, Bhilai (C.G.), India 2 Associate Professor, Department of Computer Science and Engg., CSVTU University Rungta College of Engineering & Technology, Bhilai (C.G.), India Abstract: Segmentation of noisy images is one of the most challenging problems in image analysis. In this paper, we propose a new method for image segmentation, which is able to segment all type noisy images. The performance of existing (K-means) and proposed (Firefly) algorithm was tested on three images. The experimental results prove that Firefly algorithm performs better for all types of noisy images. Keywords: Image segmentation, Image Noise, Firefly Algorithm, K-means 1. Introduction Image segmentation [2] is an important process in many computer vision and image processing applications. It divides an image into a number of discrete regions such that the pixels have high similarity in each region and high contrast between regions. Purpose of dividing an image is to further analyze each of these objects present in the image to extract some high level information. In order to facilitate practical manipulation, recognition, and object-based analysis of multimedia resources, partitioning pixels in an image into groups of coherent properties is indispensable. This process is regarded as image segmentation [3]. Noise in images represents unwanted information which degrades the image quality. Noise is defined as a process which affects the acquired image quality that is being not a part of the original image content [4] The main source of noise in digital images arises during image acquisition (digitization) or during image transmission. The principal sources of noise in the digital image are: a) The imaging sensor may be affected by environmental conditions during image acquisition. b) Insufficient Light levels and sensor temperature may introduce the noise in the image. c) Interference in the transmission channel may also corrupt the image. d) If dust particles are present on the scanner screen, they can also introduce noise in the image. We can consider a noisy image to be modelled as follows: Where f(x, y) is the original image pixel, η(x, y) is the noise term and g(x, y) is the resulting noisy pixel. [5] 2. Different Noise Models Noise [6], [7] is a random variation of image intensity and visible as grains in the image. Image noise is the random variation of brightness or color information in images produced by the sensor and circuitry of a scanner or digital camera. Different factors may be responsible for introduction of noise in the image. Image noise can be classified as:  Amplifier noise (Gaussian noise)  Salt-and-pepper noise  Shot noise (Poisson noise)  Speckle noise A. Amplifier Noise (Gaussian Noise) Gaussian noise [8] model is additive in nature and follow Gaussian distribution. Each pixel in the noisy image is the sum of the true pixel value and a random, Gaussian distributed noise value. The noise is independent of intensity of pixel value at each point. B. Salt-and-Pepper Noise The term impulse noise is also used for salt-and-pepper noise [8], [6]. Black and white dots appear in the image as a result of this noise and hence the name salt and pepper noise. This noise arises in the image because of sharp and sudden changes of image signal. This type of noise can be caused by dead pixels, analog-to-digital converter errors, bit errors in transmission, etc. C. Poisson Noise Poisson noise [6], [4] is also known as shot noise. It is a type of electronic noise. It occurs when the finite number of particles that carry energy, such as electrons in an electronic circuit or photons in an optical device, is small enough to give rise to detectable statistical fluctuations in a measurement. D. Speckle Noise Speckle noise [6], [8] is a type of granular noise that commonly exists in and causes degradation in the image quality. This noise deteriorates the quality of active radar and Synthetic aperture radar (SAR) images. Speckle noise occurs due to random fluctuations in the return signal from an object in conventional radar that is not big as single image- processing element. It increases the mean grey level of a local area. Paper ID: 020132245 1721