Purpose: Microarray experiments are important tools for high throughput gene quantification. Nevertheless, such experiments are confounded by a number of technical factors, which operate at the fabrication, target labelling, and hybridization stages, and result in spatially inhomogeneous noise. Unless these sources of error are addressed, they will propagate throughout the stages of the analysis, leading to inaccurate biological inferences. The aim of this study was to investigate whether image restoration techniques may improve the accuracy of subsequent microarray image analysis steps (i.e. segmentation and gene quantification). Materials and Methods: A public dataset of seven microarrays obtained from the MicroArray Genome Imaging & Clustering Tool (MAGIC) database were used. Each image contained 6400 spots investigating the diauxic shift of Saccharomyces cerevisiae. Restoration was based on the Wiener deconvolution. Subsequently, restored images were processed with the MAGIC tool for semi-automatic griding and segmentation. The influence of the restoration process on the accuracy of spot segmentation was quantitatively assessed by the information theoretic metric of the Kullback-Liebler divergence. Results: Pre-processing based on Wiener deconvolution increased the range of divergence (0.04 – 3.01 bits) and consequently improved the accuracy of subsequent spot segmentation. Conclusion: Information theoretic metrics confirmed the importance of image restoration as a preprocessing step that significantly improved the accuracy of subsequent segmentation, thus leading to more accurate gene quantification.