High-resolution geophysical techniques are now capable of routine assessment of sea-floor geology relevant to oil and gas exploration and production in the Gulf of Mexico to water depths of 7700 ft. Survey methods pioneered in deepwater areas off the Atlantic east coast have been significantly improved. Present practice uses satellite navigation for ship positioning and bottom-mounted telemetering transponder arrays for accurate positioning of deeply towed sensors. Deeply towed subbottom profiler and side-scan sonar systems provide very high resolution data for near-surface sediments and seafloor morphology. Digital recording provides capability for real-time image processing and enhancement.Bathymetric mapping uses surface-towed narrow-beam fathometers calibrated for water column velocity and bottom slope.Medium-penetration seismic data are displayed through a control module to reduce vertical exaggeration and improve resolution. The new techniques allow comprehensive engineering geologic evaluations of deepwater prospects in a cost-and time-effective manner.

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The image processing technique known as superresolution (SR) has the potential to allow engineers to specify lower resolution and, therefore, less expensive cameras for a given task by enhancing the base camera's resolution. This is especially true in the remote detection and classification of objects in the environment, such as aircraft or human faces. Performing each of these tasks requires a minimum image "sharpness" which is quantified by a maximum resolvable spatial frequency, which is, in turn, a function of the camera optics, pixel sampling density, and signal-to-noise ratio. Much of the existing SR literature focuses on SR performance metrics for candidate algorithms, such as perceived image quality or peak SNR. These metrics can be misleading because they also credit deblurring and/or denoising in addition to true SR. In this paper, we propose a new, task-based metric where the performance of an SR algorithm is, instead, directly tied to the probability of successfully detecting critical spatial frequencies within the scene.

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