The objective of this work was to perform a comparative evaluation of five different wavelet-based filtering techniques in the task of microarray image denoising and enhancement. Clinical material comprised microarray images collected from the Oak Ridge National Laboratory. Image processing was performed in two stages: In the first stage an Exponential Histogram Equalization filter was applied in order to increase the contrast between spots and surrounding background. In the second stage, five wavelet-based image filters (Simple Piece-Wise Linear Mapping Filter (SPWLMF), Hard Threshold filter (HTF), Wavelet Enhancement with Noise Suppression filter (WEWNSF), Garrote Wavelet Threshold filter (GWTF) and Sigmoidal Non-linear Enhancement filter (SNLEF)) were implemented for denoising and enhancing gene microarray spots. The enhancing effectiveness of the five filters was assessed by calculating the Mean-Square-Error (MSE) and the Signal-to-MSE ratio. Results showed that the image quality of the processed images was superior to that of the original images. Significant noise suppression was accomplished by the SPWLMP filter, which scored the minimum MSE and the maximum Signal-to-MSE ratio. Processing time was less than 3 seconds for 512x512 sample images. Wavelet-based processing of microarray images was found to enhance microarray images effectively, by improving the visualization of spots and by suppressing image noise.