Statistical texture retrieval in noise using complex wavelets

This paper investigates the use of complex wavelets for statistical texture retrieval in a noisy environment, in which the query image is contaminated by noise. To account for the presence of noise, the feature extraction step is based on parameter estimation in noise where features are extracted from the noisy query image by modeling the magnitude and phase of complex subband coefficients of the clean image, and relating the model's parameters to the noisy coefficients. In addition to using only the magnitude or phase which is in the form of the relative phase, we incorporate both magnitude and phase information to further improve the accuracy rate. The simulation results show the retrieval rate improvement by estimating the clean parameters from the noisy query image instead of assuming that the query image is clean. Furthermore, using both magnitude and phase of complex coefficients improves the accuracy rate from using either magnitude or phase alone, and that using complex-valued wavelets yields higher rate than using real-valued wavelets.