IJSRSET1623125 | Received : 27 May 2016 | Accepted : 19 June 2016 | May-June 2016 [(2)3: 728-732] © 2016 IJSRSET | Volume 2 | Issue 3 | Print ISSN : 2395-1990 | Online ISSN : 2394-4099 Themed Section: Engineering and Technology 728 A Novel Approach to Denoise 2D-Images from Different Noise Signals Using Combination of Various Filters Prerna Sahu Shri Shankaracharya Technical Campus, Faculty of Engineering and Technology, Bhilai, Chhattisgarh, India ABSTRACT Magnetic resonance medical images are generally corrupted by random noise from the measurement process which reduces the accuracy and reliability of any automatic analysis. Development in computerized medical image reconstruction has make medical imaging into one of the most important sub-fields in scientific imaging. The quality of digital medical images becomes an important issue with the use of digital imaging to diagnose a disease. It is necessary that medical image must be clean, sharp and noise free to obtain a best possible diagnosis. As the technology became advance the quality of digital images continue to improve, the result is in improvement in the resolution and quality of images, removing noise from these images is one of the challenging task because they could blur and mask important parameter of the images. These are different images de-noising methods each having their own advantages and disadvantages. De-noising methods are often applied to increase the signal to noise ratio (SNR) and improve image quality. The search for efficient image de-noising methods is still a valid challenge at the crossing of functional analysis and statistic. Many de-noising methods have been developed over the years, among this method, wavelet thresholding is one of the most popular approaches. In wavelet thresholding a signal is decomposed into its approximation (Low frequency) and detail (high frequency) sub-bands; since most of the image information is connected in a few large coefficients, the detail sub-bands are processed with hard or soft thresholding operations. Keywords: 2D-Images, Noise Signals, signal to noise ratio, frequency, Wavelet, DWT, SSTC, Median Filter I. INTRODUCTION Basic Noise Theory Noise is defined as an unwanted signal that interferes with the communication or measurement of another signal. A noise itself is an information-bearing signal that conveys information regarding the sources of the noise and the environment in which it propagates. The types of Noise are following:- • Amplifier noise (Gaussian noise) • Salt-and-pepper noise  Amplifier noise (Gaussian noise) The standard model of amplifier noise is additive, Gaussian, independent at each pixel and independent of the signal intensity. In color cameras where more amplification is used in the blue color channel than in the green or red channel, there can be more noise in the blue channel. Amplifier noise is a major part of the "read noise" of an image sensor, that is, of the constant noise level in dark areas of the image.  Salt-and-pepper noise An image containing salt-and-pepper noise will have dark pixels in bright regions and bright pixels in dark regions [4]. This type of noise can be caused by dead pixels, analog-to-digital converter errors, bit errors in transmission, etc. This can be eliminated in large part by