Super-Resolution and Object Detection: A Love Story - Part 1

Jake Shermeyer & Adam Van Etten

Jake Shermeyer
Nov 6, 2018 · 4 min read
60cm GSD Input (Left) — 30cm GSD Super-Resolved Output (Center) — 30cm GSD Ground Truth (Right)
The original 30cm inputs and 15cm outputs
Table 1. Average inference and training time for a 1,500 image set of 544 x 544 pixel images at native 30cm GSD resolution. RFSR used a 64GB RAM CPU and VDSR used a NVIDIA Titan Xp GPU for inference and training.
120cm GSD Input (Left) — 30cm GSD Super-Resolved Output (Center) — 30cm GSD Ground Truth (Right)
120cm GSD Input (Left) — 60cm GSD Super-Resolved Output (Center) — 60cm GSD Ground Truth (Right)

The DownLinQ

Welcome to the official blog of CosmiQ Works, an IQT Lab…

Thanks to Adam Van Etten

Jake Shermeyer

Written by

Research Scientist at CosmiQ Works

The DownLinQ

Welcome to the official blog of CosmiQ Works, an IQT Lab dedicated to exploring the rapid advances delivered by artificial intelligence and geospatial startups, industry, academia, and the open source community

Jake Shermeyer

Written by

Research Scientist at CosmiQ Works

The DownLinQ

Welcome to the official blog of CosmiQ Works, an IQT Lab dedicated to exploring the rapid advances delivered by artificial intelligence and geospatial startups, industry, academia, and the open source community

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