Abstract
Super-resolution (SR) technology provides a far promising computational imaging approach in obtaining a high-resolution (HR) image (or image sequences) from observed multiple low-resolution (LR) images by incorporating complementary information. In this paper, a three-stage SR method is proposed to generate a HR image from infrared (IR) LR Images acquired with Unmanned Aerial Vehicle (UAV). The proposed method integrates a high-level image capturing process and a low-level SR process. In this integrated process, we incorporate UAV path optimization, sub-pixel image registration, and sparseness constraint into a computational imaging framework of a region of interest (ROI). To refine ROI complementary feathers, we design an optimal flight control scheme to acquire adequate image sequences from multi-angles. In particular, a phase correlation approach achieving reliable sub-pixel image feature matching is adapted, on the basis of which an effective sparseness regularization model is built to enhance the fine structures of the IR image. Unlike most traditional multiple-frame SR algorithms that mainly focus on signal processing and achieve good performances when using standard test datasets, the performed experiments with real-life IR sequences indicate the three-stage SR method can also deal with practical LR IR image sequences collected by UAVs. The experimental results demonstrate that the proposed method is capable of generating HR images with good performance in terms of edge preservation and detail enhancement.
Highlights
Imaging devices have limited achievable resolution due to several theoretical and practical restrictions
The aim of this study is to develop a Unmanned Aerial Vehicle (UAV)-equipped IR super-resolution reconstruction strategy to explicitly account reconstruction precision and efficiency
A three-stage SR method is proposed to integrate a high-level image capturing and a lowlevel SR process, in which we incorporate UAV path optimization, sub-pixel image registration, and sparseness constraint into a computational imaging framework of a region of interest (ROI)
Summary
Imaging devices have limited achievable resolution due to several theoretical and practical restrictions. This problem remains to be challenging in the field of telemetry systems research, as it involves physical constraints due to the cameras themselves and constraints due to their operating environment, as well as other operational requirements Several quintessential methods, such as nonuniform interpolation approach [7,8,9], frequency domain approach [10,11,12,13,14], deterministic approach [15,16,17,18,19], stochastic approach [20,21,22,23] and ML-POCS hybrid reconstruction approach [24] have been developed for SR reconstruction. The input images are estimated depending on these parameters
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