Wavelet Based Random Noise Removal from Color Images Using Python

Image de-noising is an essential field in image processing, encompassing a wide range of applications. This is pre-processing task in which unwanted noise signals are removed using different techniques. Noise are unwanted signals which deteriorate the useful information from the image. These information may be edges, ridges, contours are other fine structures. For different applications these details are important. Noise signals may contaminate the image partially or completely. It depends upon the type of noise and its level. Noise may be categories according to its characteristics. The most frequent types of noise signals encountered in image processing include Additive White Gaussian Noise, Speckle noise, salt and pepper noise, Rician noise, random noise, and more. Noise signals introduced in the images during data acquisition, transmission or due to faulty location. Additive white Gaussian noise is one of the most common noise signal which affect almost all the images in a certain extent. In this chapter we apply de-noising technique which is based on wavelet thresholding. Wavelet transform is widely recognized as one of the most popular transforms in signal and image processing. It is used in various image processing applications. Thresholding is an essential component in wavelet transform, and it is commonly classified into two types: hard thresholding and soft thresholding. In the chapter we apply soft thresholding technique which outperforms hard thresholding technique.