The hierarchical property of the DWT offers the possibility of analysing a signal at different resolutions (levels) and orientations. This multiresolution analysis gives both space and frequency localization, and different orientations extract different features of the frame, such as vertical, horizontal, and diagonal information. Through wavelet analysis, an original image can be decomposed into an approximate image LL and three detail images LH, HL and HH. Using wavelet analysis on the approximate image again, four lower-resolution sub-band images will be got, and among them, the approximate image hold most of the information of the original image, while the others contain some details such as the edge. Generally speaking, edges and textures will be represented by large coefficients in the high frequency sub-bands, and they are well localized within the sub-band. In practice, wavelet analysis is performed using multilevel filter banks. The DWT of an image has two parts: an approximation part (this is an image with smaller dimensions) and a detail part (this is a set of images with smaller dimensions containing the details of the original image). Hence the DWT gives the access to the details of the original image.
