Wavelet Thresholding Algorithms for Image Denoising

Abstract

This paper talks about the wavelet thresholding algorithm for image denoising. Any data, either in the form of signals, or images contains more noise than informations. To make sense out of it, it needs denoising. For that, this paper explains algorithm that makes active use of wavelet thresholding to achieve maximum denoising. For statistical analysis matlab software is used as it comes with wavelet thresholding application. This is then used to process standard lenna image to obtain haar wavelet transform for three levels of decomposition of image. On the contrary daubechies wavelet transform is also applied to the same sample image of lenna. Using Haar Wavelet for image compression has a little bifurcation in Retained Energy and Number of Zeros along x axis. On the other hand Daubechies Wavelet compression with global thresholding on decomposition level 4 for standard image of lenna yields different trend lines between Retained Energy and Number of Zeros. Its applications vastly covers all medias such as image, video, signals, etc. to achieve maximum information. With advances in image denoising, space can be utilized more appropriately as user can be able to save space in his personal devices like mobile phones, laptops, etc. With this user can be able to use or access that free space in order to upload more data, or use it for his computational use. Keywords: Image Denoising, Thresholding, Wavelet Transform