International Journal of Ethics in Engineering & Management Education Website: www.ijeee.in (ISSN: 2348-4748, Volume 1, Issue 3, March 2014) 21 Image Filtering Noise Removal with Speckle Noise Anindita Chatterjee Dr. Chandhan Kolkata Himadri Nath Moulick Tata Consultancy Services B. C. Roy Engineering College Aryabhatta Institute of Engg & Management Kolkata – West Bengal, India Durgapur - India Durgapur - India anindita_star24@rediffmail.com Abstract— The main purpose of this paper is a brief study about the cause of the noise in the image and the removal technique of the noise from the noisy image. Images are often degraded by the noises. Noise removal is an important task in digital image processing technique. In general noise removal technique has a strong influence on the improvement of the image quality. In the field of noise reduction several linear and non-linear techniques are proposed. Linear filtering technique has a disadvantage because it is not able to effectively eliminate impulse noise as they have a tendency to blur the edges of the image. But non-linear techniques are effectively able to handle the impulse noise. Index Terms—Noise, formatting, image, Liner filtering, non-liner filtering. I. INTRODUCTION Image enhancement is very important field in image processing. It is important to reduce noises from the images before extracting some features. There is no general theory of image enhancement.[2] When an image is processed for visual interpretation, then the viewer is the best judge of how well a particular method works.[3] A certain amount of trial and error is required before a particular image enhancement approach is selected. Medical images, Satellite images are usually degraded by noise. Noise is the error which is caused in the image acquisition process, effects on image pixel and results an output distorted image. Noise reduction is the process of removing noise from the signal. Sensor device capture images and undergoes filtering by different smoothing filters and gives processed resultant image. All recording device may suspect to noise. The main fundamental problem is to reduce the noises from the color images.[4] The image analysis process can be broken into three parts which are preprocessing, data reduction, and features analysis. There may introduce noise in the image pixel mainly for three types, such as- i) Impulsive Noise ii) Additive Noise(Gaussian Noise) iii) Multiplicative Noise(Speckle Noise).[14] Noise removal is an important task in Image Processing. This section offers some ideas about various noise reduction techniques. Depending on nature of noise, such as Additive or Multiplicative Noise, there are several approaches for removal of noise from an image. Multiplicative noise is generally more difficult to remove from the images than additive noise because the intensity of the noise varies from the signal intensity (e.g. Speckle Noise). Traditionally this is achieved by Linear Processing such as Wiener Filtering.[1] Synthetic Aperture Radar (SAR) imagery uses microwave radiation so that it can illuminate the surface earth. It provides its own illumination technique. It is not affected by the cloud cover or radiation in solar illumination.[3] II. SOURCES OF NOISE IN DIGITAL IMAGES The main source of noise in digital image processing arises during the image acquisition process (sampling and digitization) or image transmission. Noise is usually measured by the percentage of the corrupted image pixel. There are several reasons to add noise in the original image depending on how the image has been created. The reasons are: I. If the image is scanned from a photograph made on film, the film grain is a source of noise. Noise may also be the result of damage to the film, or be introduced by the scanner itself. II. If the image is captured in a digital format directly, then the mechanism for gathering (the data) may introduce noise. III. Electronic transmission of image data can make noise. IV. If the device sensor is not properly opened, then the emitted light from the object cannot enter into the device lenses and that's why noise is introduced into the image. Sensor temperature and light levels are major factors in making a noise.[13] V. Dynamic range is a parameter which is used in mapping a 3D view in the image plane. If this range is high then the light intensity is scattered into a wide region which makes the picture noisy and if the dynamic range is small then more intensity is gathered on the pixels which also make the picture noisy. III. MATHEMATICAL MODEL OF SPECKLE NOISY IMAGE Mathematically noisy image can be represented as Here represents captured original image (object pixel) is the observed degraded image, represents the impulse noise of the image acquiring process