Age-related macular degeneration (ARMD) is a degenerative disease that affects the retina, and the leading cause of visual loss. In its dry form, the pathology is characterized by the progressive, centrifugal expansion of retinal lesions, called geographic atrophy (GA). In infrared eye fundus images, the GA appears as localized bright areas and its growth can be observed in series of images acquired at regular time intervals. However, illumination distortions inside and between the images make impossible the direct comparison of intensities in order to study the GA progress. In this article, we propose a new method to compensate for illumination distortion between images. All images of the series are jointly processed so that any two images have comparable gray levels. We also propose morphological processing to suppress illumination artefacts. Then we present many experiments performed on a dataset of 18 series (328 images) which were manually segmented by an ophthalmologist. Contrast measurements at the different stages of the algorithm enable us to evaluate the benefit of the processing. Moreover, we propose a first segmentation method to detect the GA growth between images and produce colored maps representing the GA evolution in the series. These preliminary segmentation results are analyzed qualitatively and quantitatively through the usual metrics. They are very promising although the segmentation method is fairly basic, demonstrating that our normalization algorithm is very efficient. This opens up the possibility of treating each series as a whole to better model the GA growth and finally understand its intimate mechanisms.