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A Study of Image Noising and Filtering Technique Surabhi1, Neha Pawar2 Research Scholar1, Assistant Professor2 SDDIET Department of Computer Sc

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A Study of Image Noising and Filtering Technique Surabhi1, Neha Pawar2 Research Scholar1, Assistant Professor2 SDDIET Department of Computer Sc DOI 10.4010/2016.1647 ISSN 2321 3361 © 2016 IJESC ` Research Article Volume 6 Issue No. 6 A Study of Image Noising and Filtering Technique Surabhi1, Neha Pawar2 Research Scholar1, Assistant Professor2 SDDIET Department of Computer Sc. Barwala Haryana, India Abstract: In the world of image processing field, removal of high density desire noise is always a famous area for research. In the sources of noise in images arise during image acquisition, digitization or transmission. Now study for removing fixed impulse noise from color images. Impulse noise is produced by errors in the data transmission generate in noisy sensors or message channels, or by errors through the data capture from digital cameras. Several nonlinear filters have been proposed for renovation of images impure by salt and pepper noise. Among these standard median filter has been recognized as trustworthy method to remove the salt and pepper noise without harmful the edge details. Keywords: Image Processing, Impulse Noise, Bilateral Filter, Image Quality Metric 1. INTRODUCTION Color images are very often ruined by impulsive noise, which Computer image processing methods mainly take two is introduced into the image by faulty pixels in the camera categories. First, the space domain processing; that is in the sensors, broadcast errors in noisy channels, poor lighting image gap of the image processing. The other is the image conditions and aging of the storage material [1]. The spatial domain. It should be use rate domain through the suppression of the disturbances introduce by the impulsive orthogonal. In the world of image processing field, removal noise is indispensable for the success of additional stages of of high density desire noise is always a famous area for the image processing pipeline. Gray scale image cover no research. The sources of noise in images arise during image colour information. They represent the brightness of the acquisition, digitization or transmission. Now study for image. This image contains 8 bits/pixel, which means it, can removing fixed impulse noise from color images. Impulse have up to 256 (0-255) different brightness levels. A ‘0’ noise is produced by errors in the data transmission generate represents black and ‘255’ denotes white. In among values in noisy sensors or message channels, or by errors through from 1 to 254 represent the different gray levels. As they the data capture from digital cameras. Several nonlinear contain the intensity information, they are also denoted to as filters have been proposed for renovation of images impure intensity images. Colour images are considered as three band by salt and pepper noise. Among these standard median filter monochrome images, where each band is of a different has been recognized as reliable method to remove the salt colour. Each band offers the brightness information of the and pepper noise without harmful the edge details. corresponding spectral band. Classic colour imagery are red, green and blue images and are also referred to as RGB Transformation in various frequency domain. Next, do images. This is a 24 bits/pixel image. reversal processing further and then it can be stop processing for image. It is also based on the actual characteristics of the ii. Image Filters image, noise and spectral division of the demographic A variety of adaptive filtering techniques has been proposed characteristics of the law. Scientists derived many de-noising for enhancing images degraded by noise [8:34]. By adjusting approaches. One of the most sensitive ways of noise energy their parameters, depending on local characteristics of the is generally concentrated in high-frequency and spectral input image, these filters preserve sharp continued changes images situated in a incomplete range of this characteristic. (edges) in the image while reducing noise. All of the filters And then low-pass filtering approach is used to de-noising or are useful for additive white noise suppression, but are smoothing the image handing out. This is the first class of generally ineffective in eliminating impulsive noise that image processing methods. Another way is processing in the appears as very large spikes of short duration. Various filters incidence domain (such as: Fourier transform, wavelet are discussed here available for the impulse noise transform). Image denoising is often used in the field of elimination. By adjusting their parameters, dependent on photography or publishing where an image was somehow local characteristics of the input image, these filters tainted but needs to be improved before it can be printed. For reservation sharp sustained changes (edges) in the image this type of request we need to know something about the while reducing noise. poverty process in order to develop a model for it. When we have a model for the degradation process, the opposite 2.1.1 Bilateral Filter process can be applied to the image to restore it back to the original form. This type of image restoration is frequently Bilateral Filter is a nonlinear, non-iterative technique, which used in space exploration to help eliminate arte facts uses both range filtering and domain filtering instead of only generated by mechanical jitter in a spaceship or to domain filtering used by the conventional techniques. compensate for distortion in the optical system of a telescope. Bilateral filter is capable of smoothing the image and Image denoising finds applications in fields such as preserving edges as well. The bilateral filter can also be astronomy defined as a weighted average of nearby pixels, in a custom very similar to Gaussian convolution. The difference is that International Journal of Engineering Science and Computing, June 2016 6892 http://ijesc.org/ the bilateral strain takes into account the change in value with iii. Noise in Images the neighbors to protect edges while smoothing. During the capture, transmission, processing or acquisition of an image it can have many kind of variations in its original The key idea of the bilateral filter is that for a pixel to affect form, this variation is frequently random and has no another pixel, it should not only inhabit a nearby location but particular pattern. In many cases, it reduces image quality. also have a similar value. Bilateral filter overcomes the This chance variation in image is called noise. Generally limitations of Gaussian low pass filtering and anisotropic noise gives an image an undesirable appearance, the most diffusion techniques of image smoothing. As bilateral significant factor that noise can cover and reduce is the filtering is the combination of both range and domain visibility of certain features within the image. The noise filtering. Hence in this both operations like good filtering is present in image can be either in additive form or in achieved at the boundary of images because of domain multiplicative form. These both forms can be represented as filtering and crisp edges can be preserved with the help of below: range filtering. When applied to the color images bilateral filter operates on the three bands of color (RGB) at same time Additive noise equation- w(x,y) = s(x,y) + n(x, y) Eq. (1.1) and it averages the perceptually similar colors together, hence Multiplicative noise equation- w(x, y) = s(x, y) n(x, y) no artifacts occur at the edges, which occurs when three Eq.(1.2) bands are filtered separately [5]. In the above equations s(x, y) represents the original signal, n(x, y) is the noise introduced in signal, w(x, y) is the image corrupted by noise and (x, y) is the pixel location. There are different sources of noise in a digital image, depending upon sources noise can be: Dark current noise, Shot noise, Amplifier noise and Quantization noise, usually following types of noises are most common in image processing: Gaussian Noise Impulse noise (Salt and Pepper Noise) Speckle Noise 3.1.1. Gaussian Noise Gaussian noise is a noise which has Gaussian distribution, which has a bell shaped probability distribution function. This noise is evenly distributed over the signal. This means that in the loud image each pixel has a value which is the sum of the true pixel value and a random Gaussian spread noise Fig2.1 Edge preserving by using bilateral filter during value. The probability distribution function of Gaussian noise denoising. [5] is F(g) = Eq. (1.3) 2.1.2 Median Filter The low pass filtering methods like averaging filter do blur of In the above equation g represents the gray level, m is the image, hence it also distortions the edges of image with the mean or average, and σ is the standard deviation of the noise. noise. Low pass filter methods works well for Gaussian The following graph shows the distribution: sound but they cannot remove salt and pepper noise from the image, Hence for the removal of salt and pepper noise from image a non-linear filter called Median Filter (MF) is used. It swaps the gray level of all the pixels in the neighborhood. Median filtering is very effective in eliminating sharp ‘spikes’ from an image. When sound is introduced in an image low pass filtering just shapes the noise where the Median filtering succeed sin removing most of the noise [7]. If necessary, several Passes with a median filter may be needed. The median filtering actually military pixels with Fig.3.1Probability thickness Function for Gaussian noise. very distinct gray levels to have a gray level that is more similar to its neighbors. 3.2.2 Impulse (Salt-and-Pepper) Noise Model 2.1.3 Trimmed Median Filter The PDF of (bipolar) impulse noise is given by Trimmed Median Filter for noise detecting and removing, the pa for z a whole of the image is scan and processed pixel by pixel.
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