The nature of the noise removal problem depends on the type of the noise corrupting the image. Each pixel in noisy image is the sum of true pixel value and a random gaussian distributed noise value [4]. Gaussian noise Gaussian noise is evenly distributed over the signal. There are different types of noise is present and each type of noise has different characteristics. The common types of are: II.1: Salt Pepper Noise: Salt and pepper noise is an impulse type of noise. We will show how we can generate these types of noise and add them to clean images. The lower image is the histogram for noisy image. In general the results of the noise removal have a strong influence on the quality of the image processing techniques. It is actually Several techniques for noise removal are well established in color image processing. Because it has only 2 colours, there are just two spikes. In image processing, noise produces an image that may consist of uneven lines, blurred object, distortion of Below: The image with gaussian noise. Different noise models including additive and multiplicative types are discussed in the paper.Selection of the denoising algorithm is application dependent. ECE/OPTI533 Digital Image Processing class notes 239 Dr. Robert A. Schowengerdt 2003 IMAGE NOISE I TYPES OF NOISE • photoelectronic • photon noise • thermal noise • impulse • salt noise • pepper noise • salt and pepper noise Above: The original image. TYPES OF NOISE:-Noise is an unwanted or distort signal that may corrupt the quality or the originality of the image. The histogram for each of these images is: The upper image is the histogram for the original image. Our main concern is to remove certain kind of noise. Digital image processing techniques can also include restoration. Noise has been produced in the image due to transmission. 3.2. Noise is always presents in digital images during image acquisition, coding, transmission, and processing steps. Temporal vs. Spatial Noise • It is common to assume that: – spatial noise in an image is consistent with the temporal image noise – the spatial noise is independent and identically distributed • Thus, we can think of a neighborhood of the image itself as approximated by an additive noise process This paper reviews the existing denoising algorithms and performs their comparative study. Then, we will show how we can filter these images using a simple median filter. is an important task in image processing. Thus, the main source of image de-noising is Image Digitization. Noise, which may show up as random dots or streaks, can be eliminated through replacement techniques. So we have to first identify certain type of noise and apply different algorithms to remove the noise. Some scanned images may pick up tears or lines. Noise is always presents in digital images during image acquisition, coding, transmission, and processing steps. the types of disturbance, the noise can affect the image to different extent. 3.1. The main challenge in digital image processing is to remove noise from the original image. Digital Image Processing using OpenCV (Python & C++) Highlights: We will give an overview of the most common types of noise that is present in images. When noise is added, notice how "gaussian-like" the histogram becomes.