1.1 GENERAL INFORMATION In digital image processing, computer image manipulation is a relatively modern outgrowth of the ancient human attraction to visual inspirations. In recent history, it has been used for all sorts of imagery, with varying degrees of success. The essential subjective influence of pictorial representations attracts a disproportionate amount of concentration from the scientist and the end user. Image processing suffers from myths, misunderstandings, misconceptions and misinformation. The main goal of image compression is to reduce the storage requirements for digital imaging and the time required for image transfer, but at the expense of compression and decompression times. Image restoration is the process of removing noise in an image and evaluating the clean original image. Noise can come in various forms, such as motion blur, noise, and incorrect camera focus. Image segmentation is the process of partitioning a digital image into sets of disjoint and connected pixels, one of which corresponds to the background and the rest to objects in the image. Segmentation matching can be used to locate known-looking objects in an image to look for specific patterns. Image processing is a vital supervision under which different aspects of optics, electronics, mathematics, photography and computer technology are reduced. Image processing is plagued by contradictory controls and jargon taken from different fields. Digital image processing involves the acquisition, enhancement, restoration, compression, segmentation, representation and description of images. Image acquisition is the first process in digital image processing and can be broadly explained as the action of retrieving an image from a source, usually a hardware-based source. The purpose of the image......middle of the paper......IDWT is horizontal, vertical and diagonal images, used only Multiresolution representations are very effective for analyzing the information content of images. In studying the properties of the operator that approximates a signal at a given resolution, the difference in information between the approximation of a signal at resolutions 2' + ' and 2j can be extracted by decomposing this signal on an orthonormal wavelet basis of L* ( R"). In LL(R), a wavelet orthonormal basis is a family of functions, constructed by dilating and translating a single function t+r (xl. This decomposition defines an orthogonal multiresolution representation called a wavelet representation. It is computed with a basis pyramidal convolution-based algorithm with quadrature mirror filters For images, the wavelet representation differentiates different spatial orientations [1].