A Comparison of Deep Neural Networks for Monocular Depth Map Estimation in Natural Environments Flying at Low Altitude.

Currently, the use of Unmanned Aerial Vehicles (UAVs) in natural and complex environments has been increasing, because they are appropriate and affordable solutions to support different tasks such as rescue, forestry, and agriculture by collecting and analyzing high-resolution monocular images. Autonomous navigation at low altitudes is an important area of research, as it would allow monitoring parts of the crop that are occluded by their foliage or by other plants. This task is difficult due to the large number of obstacles that might be encountered in the drone's path.

Driving Maximal Frequency Content and Natural Slopes Sharpening for Image Amplification with High Scale Factor.

Background: In this paper, a method for adaptive Pure Interpolation (PI) in the frequency domain, with gradient auto-regularization, is proposed.

Methods: The input image is transformed into the frequency domain and convolved with the Fourier Transform (FT) of a 2D sampling array (interpolation kernel) of initial size L × M. The Inverse Fourier Transform (IFT) is applied to the output coefficients and the edges are detected and counted.