Super-resolution enhancement of UAV images based on fractional calculus and POCS

Abstract

A super-resolution enhancement algorithm was proposed based on the combination of fractional calculus and Projection onto Convex Sets (POCS) for unmanned aerial vehicles (UAVs) images. The representative problems of UAV images including motion blur, fisheye effect distortion, overexposed, and so on can be improved by the proposed algorithm. The fractional calculus operator is used to enhance the high-resolution and low-resolution reference frames for POCS. The affine transformation parameters between low-resolution images and reference frame are calculated by Scale Invariant Feature Transform (SIFT) for matching. The point spread function of POCS is simulated by a fractional integral filter instead of Gaussian filter for more clarity of texture and detail. The objective indices and subjective effect are compared between the proposed and other methods. The experimental results indicate that the proposed method outperforms other algorithms in most cases, especially in the structure and detail clarity of the reconstructed images.

Keywords:

• Unmanned aerial vehicle (UAV) image
• super-resolution
• fractional calculus
• Projection onto Convex Sets (POCS)
• Scale Invariant Feature Transform (SIFT)

Acknowledgments

We would like to thank Kai Yan, Xihan Mu, Gaiyan Ruan, and Yingji Zhou, School of Geography, Beijing Normal University, for part of the UAV images used in this study are chosen from their article “Dataset of UAV remote sensing images in forest district of the Genhe River Basin in 2013”.