Image Analysis Using Biorthogonal Wavelet

The main objective is to investigate the still image compression and de-noising of a gray scale image using wavelet theory at different decomposition and threshold levels. The “Image Analysis Using Biorthogonal Wavelet” is implemented in software using MATLAB Wavelet Toolbox and 2-D DWT technique. The experiments and simulation is carried out on .jpg format images. The scope of the work involves knowing the Biorthogonal wavelet on compression and denoising, image clarity, to find the effect of decomposition & threshold levels and energy retaining and lost. Therefore, the image recovery is good and clarity, but the percentage of compression and retaining the energy is different. In order to quantify the performance of the denoising, a random noise is added to the still image and given as input to the denoising algorithm, which produces an image close to the original image. The significant advantage of using wavelets for image processing can be used in applications in which fourier methods are not well suited, like progressive image reconstruction