  Forward-Looking Sonar (FLS) has started to gain attention in the field of
near-bottom close-range underwater inspection because of its high resolution
and high framerate features. Although Automatic Target Recognition (ATR)
algorithms have been applied tentatively for object-searching tasks, human
supervision is still indispensable, especially when involving critical areas. A
clear FLS mosaic containing all suspicious information is in demand to help
experts deal with tremendous perception data. However, previous work only
considered that FLS is working in an ideal system configuration, which assumes
an appropriate sonar imaging setup and the availability of accurate positioning
data. Without those promises, the intra-frame and inter-frame artifacts will
appear and degrade the quality of the final mosaic by making the information of
interest invisible. In this paper, we propose a novel blending method for FLS
mosaicing which can preserve interested information. A Long-Short Time Sliding
Window (LST-SW) is designed to rectify the local statistics of raw sonar
images. The statistics are then utilized to construct a Global Variance Map
(GVM). The GVM helps to emphasize the useful information contained in images in
the blending phase by classifying the informative and featureless pixels,
thereby enhancing the quality of final mosaic. The method is verified using
data collected in the real environment. The results show that our method can
preserve more details in FLS mosaics for human inspection purposes in practice.
