A study to determine the optimum design of a photographic film for the lunar surface hand-held camera Final report

Design, and processing of photographic film for lunar surface hand operated camer

Learning to Dehaze from Realistic Scene with A Fast Physics-based Dehazing Network

Dehazing is a popular computer vision topic for long. A real-time dehazing method with reliable performance is highly desired for many applications such as autonomous driving. While recent learning-based methods require datasets containing pairs of hazy images and clean ground truth references, it is generally impossible to capture accurate ground truth in real scenes. Many existing works compromise this difficulty to generate hazy images by rendering the haze from depth on common RGBD datasets using the haze imaging model. However, there is still a gap between the synthetic datasets and real hazy images as large datasets with high-quality depth are mostly indoor and depth maps for outdoor are imprecise. In this paper, we complement the existing datasets with a new, large, and diverse dehazing dataset containing real outdoor scenes from High-Definition (HD) 3D movies. We select a large number of high-quality frames of real outdoor scenes and render haze on them using depth from stereo. Our dataset is more realistic than existing ones and we demonstrate that using this dataset greatly improves the dehazing performance on real scenes. In addition to the dataset, we also propose a light and reliable dehazing network inspired by the physics model. Our approach outperforms other methods by a large margin and becomes the new state-of-the-art method. Moreover, the light-weight design of the network enables our method to run at a real-time speed, which is much faster than other baseline methods

Tracking icebergs with time-lapse photography and sparse optical flow, LeConte Bay, Alaska, 2016–2017

We present a workflow to track icebergs in proglacial fjords using oblique time-lapse photos and the Lucas-Kanade optical flow algorithm. We employ the workflow at LeConte Bay, Alaska, where we ran five time-lapse cameras between April 2016 and September 2017, capturing more than 400 000 photos at frame rates of 0.5–4.0 min−1. Hourly to daily average velocity fields in map coordinates illustrate dynamic currents in the bay, with dominant downfjord velocities (exceeding 0.5 m s−1 intermittently) and several eddies. Comparisons with simultaneous Acoustic Doppler Current Profiler (ADCP) measurements yield best agreement for the uppermost ADCP levels (∼ 12 m and above), in line with prevalent small icebergs that trace near-surface currents. Tracking results from multiple cameras compare favorably, although cameras with lower frame rates (0.5 min−1) tend to underestimate high flow speeds. Tests to determine requisite temporal and spatial image resolution confirm the importance of high image frame rates, while spatial resolution is of secondary importance. Application of our procedure to other fjords will be successful if iceberg concentrations are high enough and if the camera frame rates are sufficiently rapid (at least 1 min−1 for conditions similar to LeConte Bay).This work was funded by the U.S. National Science Foundation (OPP-1503910, OPP-1504288, OPP-1504521 and OPP-1504191).Ye