Removal of noise is an essential step in the preprocessing of microarray images for obtaining better quality gene expression measurements. Wavelet-based methods for denoising of images are very successful. However, for cDNA microarray images, existing methods are not efficient because they fail to take into account the signal correlation as well as the noise correlation that exists between the wavelet coefficients of the two channels. In this talk, I consider the development of efficient wavelet-based noise reduction algorithms for cDNA microarray images that take into account these inter-channel dependencies by ‘jointly’ estimating the noise-free coefficients in both the channels. The algorithms are developed using two types of wavelet transforms, namely, the frequently-used discrete wavelet transform and the complex wavelet transform. Linear minimum mean squared error and maximum a posteriori estimation techniques are used to derive bivariate estimators for the noise-free coefficients of the red and green channel images by utilizing appropriate joint probability density functions for the image coefficients as well as the noise coefficients of the two channels. Extensive experimentations are carried out on a large set of cDNA microarray images to evaluate the performance of the proposed denoising methods as compared to the existing ones. Results are presented which show that the proposed methods lead to improved noise reduction performance and more accurate estimation of gene expression levels.