The general topic of this thesis is to reconstruct the background scene from a burst of images in presence of masks. We focus on the background detection methods as well as on solutions to geometric and chromatic distortions introduced during photography. A series of process is proposed, which consists of geometric alignment, chromatic adjustment, image fusion, and defect correction. We consider the case where the background scene is a flat surface. The geometric alignment between a reference image and any other images in the sequence, depends on the computation of a homography followed by a bilinear interpolation. The chromatic adjustment aims to attach a similar contrast to the scene in different images. We propose to model the chromatic mapping between images with linear approximations whose parameters are decided by matched pixels of SIFT.These two steps are followed by a discussion on image fusion. Several methods have been compared. The first proposition is a generation of the typical median filter to the vector range. It is robust when more than half of the images convey the background information. Besides, we design an original algorithm based on the notion of a clique. It serves to distinguish the biggest cloud of pixels in RGB space. This approach is highly reliable even when the background pixels are the minority.During the implementation, we notice that some fusion results bear blur-like defects due to the existence of geometric alignment errors. We provide therefore a combination method as a complementary step to ameliorate the fusion results. It is based on a comparison between the fusion image and other aligned images after applying a Gaussian filter. The output is a mosaic of patches with clear details issued from the aligned images which are the most similar to their related fusion patches.The performance of our methods is evaluated by a data set containing extensive images of different qualities. Experiments confirm the reliability and robustness of our design under a variety of photography conditions.