The acoustic lens is a high-resolution, forward-looking sonar for three dimensional (3-D) underwater imaging. We discuss processing the lens data for recreating and visualizing the scene. Acoustical imaging, compared to optical imaging, is sparse and low resolution. To achieve higher resolution, we obtain a denser sample by mounting the lens on a moving platform and passing over the scene. This introduces the problem of data fusion from multiple overlapping views for scene formation, which we discuss. We also discuss the improvements in object reconstruction by combining data from several passes over an object. We present algorithms for pass registration and show that this process can be done with enough accuracy to improve the image and provide greater detail about the object. The results of in-water experiments show the degree to which size and shape can be obtained under (nearly) ideal conditions.

Reconstruction of 3D scenes using data from an acoustic imaging sonar is addressed. The acoustic lens is described, and issues concerning underwater 3D scene reconstruction from the lens data are examined. Two methods for visualizing objects in an acoustic snapshot of the ocean are discussed: mathematical morphology and a synthesis of 3D digital imaging with volume rendering.>

The breathing gas of divers is a pressurized mixture of helium and oxygen that distorts the divers' speech. The formant frequencies and bandwidths shift upward in a nonlinear manner while the pitch period remains relatively unchanged. Commercial unscramblers (voice enhancers) work in the time domain, sampling segments of diver speech and expanding them. This technique does enhance diver speech, but not enough for reliable communication. We have implemented an algorithm that operates in the frequency domain and runs in real time on an Analogic AP400 array processor. The algorithm transforms helium speech segments into the frequency domain, compresses the formants back to normal speech values, and then transforms the corrected speech segments back into the time domain. Identical speech material was processed by both the time- and frequency-domain unscramblers and the results are compared.