In in-line digital holography, the background of the recorded images is sometimes much higher than the signal of interest. It can originates, for example, from the diffraction of dusts or fringes coming from multiple reflexions in the optical components. It is often correlated, nonstationary and not constant over time. Detecting a weak signal superimposed over such a background is challenging. Detection of the pattern then requires a statistical modeling of the background. In this work, spatial correlations are locally estimated based on several background images. A fast algorithm that computes detection maps is derived. The proposed approach is evaluated on images obtained from experimental data recorded with a holographic microscope.