For using smooth probability density function to retrieve wavelet coefficient histogram and coefficient module histogram, parameter estimation is complicated, which results in hard to retrieve the texture features effectively. A texture image retrieval method using double density dual tree complex wavelet Refined Histogram(RH) model is proposed. By analyzing the principle of double density dual tree complex wavelet transform (DD-DT CWT) and the inherent relationship between the nonuniform quantizer and RH model, the RH model is extended to retrieve the DD-DT CWT coefficient and the coefficient histogram feature. The RH is used to model the magnitude of the DD-DT CWT. The RH parameters for all magnitude of complex coefficients forms the signature of an image. Image similarity measurement is accomplished by using the Kullback-Leibler divergences . The proposed method combines the advantages of the RH model and the shift-invariant DD-DT CWT. The experiment results show that the proposed methods yields higher retrieval rate than using the General Gaussian Density(GGD) model to fit with the real part or imaginary part of coefficients, and is better than using the Gamma PDF to fit with the magnitude of coefficients.