Digital images play a vital role in image processing which have sundry no of applications in the field of medical science, engineering, space science, agricultural science etc. As a large no of scientific applications are carried out through the help of digital images so it would be quite difficult to get the accurate result if those images are corrupted by noises. Noises are unwanted information in images which arises due to several factors such as inaccurate analog to digital conversion, statistical quantum fluctuation of camera sensors, [Gonzalez, 2007] heat production in sensors and so on. Image noise is random (not present in the object imaged) variation of brightness or color information in images, and is usually an aspect of electronic noise. It can be produced by the sensor and circuitry of a scanner or digital camera. Image noise can also originate in film grain and in the unavoidable short noise of an ideal photon detector. Image noise is an undesirable by-product of image capture that adds spurious and extraneous information. Noise can introduce by transmission errors and compression. So noise reduction is most important task for improve quality of image. Denoising technique is often a necessary and the take first step, before analyzed the image data. It is important apply denoising technique to compensate for such data corruption. Denoising techniques still remains big challenge for researchers because noise removal introduced artifacts and causes blurring of an images. Image Denoising techniques depend on what type of noise occurred in image like Gaussian noise, impulse noise; speckle noise etc.
