Texture is an important attribute in the image, which provides substantial information for the recognition and interpretation of this image. The basic research problems of the image texture are texture perception, texture analysis, texture synthesis, sharp from texture and application of texture. The image texture analysis provides the strong foundation for texture image classification, texture image segmentation and texture-based image retrieval, which are important research fields in the image processing and computer vision. They have been used in defect detection, medical image and remote sensing image analysis, document image segmentation, biometric identification and content-based image retrieval.The early methods analyze the texture at a single scale. However, large numbers of psychophysical experiments and recent results suggest that the multi-channel analysis of the texture not only accord with the manner in which the image is analyzed by human visual system, but also bring better performance. Wavelet is a new multiresolution analysis tool. It is appropriate for texture image processing because of its spatial-frequency localization performance and diversity. This thesis aims to study the application of wavelet in texture image processing. The main innovative contributions of this dissertation are as follows:Firstly, three texture image classification algorithms based on the discrete wavelet frame modulus extrema are proposed. The first one is based on the density of the discrete wavelet frame modulus extrema. Because this algorithm only include the first order statistical property, but not take the locations of the modulus extrema into account, the second scheme based on the co-occurrence matrix derived form the discrete wavelet frame modulus extrema is proposed, which includes the partial location information extracted from the co-occurrence matrix of the modulus extrema, and so improves the classification performance. The above two algorithms take the modulus maximum and the modulus minimum together as a feature component, which decreases the diversity of the texture feature and so the discrimination power. The third algorithm takes the modulus maximum and the modulus minimum as two feature components. The