International Journal of Computer Engineering and Applications, TYPES OF NOISE IN DIGITAL IMAGE PROCESSING

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1 International Journal of Computer Engineering and Applications, Volume XI, Issue IX, September 17, ISSN TYPES OF NOISE IN DIGITAL IMAGE PROCESSING 1 RANU GORAI, 2 PROF. AMIT BHATTCHARJEE 1 M.Tech in CSE sarojkumar3115@gmail.com 2 Professor in RVSCET, Jamshedpur amit23jsr@gmail.com ABSTRACT Digital images act as a major role in day to day life as it is being used in different user applications such as satellite television, magnetic resonance imaging, and computer tomography as well as in areas of research and technology such as geographical information systems and astronomy. Unfortunately reference images may get corrupted with noise during acquisition, transmission, or retrieval from storage media. Some of the cases each and every pixel of image is vital and if by any means some of the portion of the image is lost or noised then it becomes totally value less. But unfortunately images inherently contain complex type of noise, originating from two distinct sources, such as the set of assorted devices involved in the acquisition, transmission, storage and display of the image and noise arising from the application of different types of quantization, reconstruction and enhancement algorithms. This paper discusses various noises like Gaussian noise, Salt and Pepper, Speckle noise and Poisson noise, etc. [1] INTRODUCTION Digital images play an important role both in daily life applications such as satellite television, magnetic resonance imaging, computer tomography as well as in areas of research and RANU GORAI, AND AMIT BHATTCHARJEE 1

2 TYPES OF NOISE IN DIGITAL IMAGE PROCESSING technology such as geographical information systems and astronomy. Some of the cases each and every pixel of image is vital and if by any means some of the portion of the image is lost or noised then it becomes totally value less. Noise represents unwanted information which deteriorates image quality. Noise is a random variation of image intensity. Noise can be explained as pixels within the picture present different intensity values rather than correct pixel values. Origin of noise can be from physical nature of detection processes and has many specific forms and causes. It can be said that noise is a process which affects the image obtained and is not a part of the scene. Noise in digital image may come from various sources. The acquisition process for digital images converts optical signals into electrical signals and then into digital signals, is one process by which the noise is introduced in digital images. The above conversion process experiences fluctuations and each of these steps adds a random value to the resulting intensity of a given pixel. Some sources of Noise are: 1. When the image is scanned from a photograph made on film, here noise can be produced from the damaged film or from the scanner used to take the image. 2. If the image is taken directly in a digital format the mechanism for gathering the data can introduce noise. 3. Insufficient light is also a cause of noise due to which sensor temperature may introduce the noise. 4. Scratches and dust particles present in the scanner screen is also a cause of creating noise. 5. Interference in transmission channel during electronic transmission can create noise in the original image. [2] ADDITIVE AND MULTIPLICATIVE NOISES Noise is undesired information that contaminates the image. Mostly images are corrupted with noise modelled with either a Gaussian, uniform, or salt or pepper distribution. Another typical noise is a speckle noise, which is multiplicative in nature. Noise is present in an image either in an additive or multiplicative form. The digital image acquisition process converts an optical image into a continuous electrical signal that is then, sampled. At every step in the process there are fluctuations caused by natural phenomena, adding a random value to the exact brightness value for a given pixel. Image multiplication is the variation in brightness of the image. Gaussian Noise: It is certain that every imaging method inherently involves noise. Many dots can be spotted in a Photograph of image taken with a digital camera due to low lighting conditions or the machine hardware problem. Actually this type of noise is the uniform Gaussian noise. Gaussian noise is evenly distributed over the signal. This means that each pixel in the noisy image is the sum of the true pixel value and a random Gaussian distributed noise value. Gaussian noise is also known as Amplifier noise. RANU GORAI, AND AMIT BHATTCHARJEE 2

3 International Journal of Computer Engineering and Applications, Volume XI, Issue IX, September 17, ISSN Figure : Gaussian Noise Image Salt and Pepper Noise: On loss of reception or retrieve any image from the storage device random black and white snowlike patterns can be seen on the images. This type of noise is called Salt & Pepper noise. Salt and pepper noise is an impulse type of noise, which is also referred to as intensity spikes. This is caused generally due to errors in data transmission. An image containing Salt and Pepper noise will have dark pixels in bright regions and vice versa. In Salt and Pepper noise pixels in the image are very different in colour or intensity unlike their surrounding pixels. The corrupted pixels are set alternatively to the minimum or to the maximum value, giving the image a salt and pepper like appearance. For images corrupted by salt and pepper noise the noisy pixels can take only the maximum and the minimum values in the dynamic range. It is found that an 8- bit image, the typical value for pepper noise is 0 and for salt noise it is 255. The salt and pepper noise is generally caused by malfunctioning of pixel elements in the camera sensors, faulty memory locations, or timing errors in the digitization process. Salt and Pepper noise is sometimes known as Impulse noise or Spike noise or Random noise or Independent noise. RANU GORAI, AND AMIT BHATTCHARJEE 3

4 TYPES OF NOISE IN DIGITAL IMAGE PROCESSING Figure : Salt and Pepper Noise Image Speckle Noise: Speckle noise can be modelled by random values multiplied by pixel values so it is known as multiplicative noise. Speckle noise affects all coherent imaging systems including medical ultrasound. Medical images are usually corrupted by noise in its acquisition and Transmission. The existence of Speckle Noise affects the tasks of individual interpretation and diagnosis. Speckle noise is a major problem in some Radar applications. Speckle noise is mainly present in Ultrasound image, Satellite images. Figure : Speckle Noise Image RANU GORAI, AND AMIT BHATTCHARJEE 4

5 International Journal of Computer Engineering and Applications, Volume XI, Issue IX, September 17, ISSN Poisson Noise: Poisson noise is the noise that is caused when number of photons sensed by the sensor is not sufficient to provide detectable statistical information. This noise exists because a phenomenon such as light and electric current consists of the movement of discrete packets. Poisson noise may be dominated when the finite number of particles that carry energy is sufficiently small so that uncertainties due to the Poisson distribution, which describe the occurrence of independent random events, are of significance. Magnitude of Poisson noise increases with the average magnitude of the current or intensity of the light. Figure : Poisson Noise Image [3] CONCLUSION In this paper, we have discussed what noise is and how it is present in images when the image is acquired or send through any transmission medium. Causes of noise and its sources are discussed to get the clear idea about noise. In Image processing it is very important to know the type of noise present in any image, so that it can be noise free image. Noise is not good in any image. Different types of noises having different effects on image and to minimize the effect every noise should be identified properly. In space technology the distance is very high so while transmission of images get noisy that images should give clear idea of space research. So here noise should get identified and removed. REFERENCES 1. Rafael. C. Gonzalez, Richard E. Woods processing, 3 rd edition, Pearson education. RANU GORAI, AND AMIT BHATTCHARJEE 5

6 TYPES OF NOISE IN DIGITAL IMAGE PROCESSING 2. Rohit Verma and Jahid Ali - A comparative study of various types of image noise and efficient noise removal techniques, International journal of advanced research in computer science and software engineering, volume 3, issue 10 October Sukhjinder Kaur - Noise Types and Various Removal Techniques, International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) Volume 4, Issue 2, February S. Sudha, G. R. Suresh and R. Sukanesh - Speckle Noise Reduction in Ultrasound Image by Wavelet Thresholding based on Weighted Variance, International Journal of Computer Theory and Engineering, Vol. 1, No. 1, April 2009, Milindkumar V. Sarode and Prashant R. Deshmukh - Reduction of Speckle Noise and Image Enhancement of Images Using Filtering Technique, International Journal of Advancements in Technology ISSN RANU GORAI, AND AMIT BHATTCHARJEE 6