Texture discrimination in noise using wavelets

In this paper the use of wavelets for classifying noisy textures is presented. The textures are decomposed using wavelets. The coefficients are thresholded based on a residual energy criterion. Restoration involves thresholding the wavelet coefficients only to a level at which the textures can be discriminated. The decomposition and thresholding is stopped when the energy based criterion is satisfied. A classifier is then used to discriminate the textures. Uniform, Gaussian, speckle and salt & pepper noise are added to the textures. Classification and segmentation experiments are conducted on photographic textures and remote sensing images. The algorithm performs well with all the types of noise, upto SNR's as low as 0 dB. The method is adaptive to any type of noise and gives improved performance compared to the available methods for texture discrimination in the presence of noise.