Salt And Pepper Noise Case Study

CHAPTER -1
INTRODUCTION
The outbreak of digital image processing in 1920’s has revolutionalised the communication process [l]. The images were the first language used by the humans for interaction. The images in the form of pictures were carved on stone which became the first language for communication and these carvings were available for the future generation. With the technical advancements in recent times; image processing techniques has became an important part of the communication system. From the medieval time to modern era images have always been an important source of information. Image acquisition, their storing and processing are becoming important applications in our routine life. Denoising and image smoothening have remained a

This type of noise is caused due to noisy transmission channels, malfunctioning of camera sensors pixels. The affected noisy pixels can take only two values i.e. maximum and minimum in the dynamic range, giving the appearance of dark spots in the white region and white spots in the dark region of an image while the unaffected region remain unchanged. For an 8 bit image the range is 0 and 255.Salt-and-pepper noise with variance 0.03 is shown in the Figure 1.1. Figure 1.1: Salt-and Pepper noise (b) Gaussian Noise
Gaussian noise is a statistical noise which is evenly distributed over the image. The Gaussian noise has probability density function (PDF) equal to normal distribution. This means each pixel in the corrupted image is the sum of original pixel value and the randomly distributed Gaussian noisy pixel values.
The probability density function of a Gaussian random variable g is given by:
F (g) = 1/(σ√2π) e^(〖-(g-μ)〗^2/〖2σ〗^2 ) (1.3)
Where, g represents the grey level, µ is the mean value and σ is the standard deviation [3].
Figure 1.2 (a) shows the original image and (b) shows the Gaussian noise with mean value as 0 and variance as