Suppression of the Background with Drastic Brightness Jumps in a Sequence of Images of Dynamic Small-Size Objects

Abstract

Among other problems, systems of space monitoring of the near-Earth space include detection of moving low-contrast objects in images with a powerful spatially nonstationary background significantly exceeding random (in most cases, weakly correlated) noise. The most effective method of increasing the signal-to-noise ratio under the conditions of different velocities of the objects and background is interframe processing of a sequence of images, which ensures suppression of the background component in the current frame by means of subtracting its estimate obtained from the previous frames. The problem is the presence of a priori unknown motion of the background, leading to significant errors in estimate formation in the regions of its sudden changes. The algorithm of interframe processing is studied, which allows one to estimate moderate local motions of the background and to compensate for them down to fractions of the sampling step. Results of full-scale modeling are presented, which demonstrate the possibility of background component suppression down to the noise level even in regions with its drastic changes.