Abstract
Nowadays, industrial video synthetic aperture radars (ViSARs) are widely used for aerial remote sensing and surveillance systems in smart cities. A main challenge of a group of networked ViSAR sensors in an IoT-based environment is low bandwidth of wireless links for communicating big video data. In this research, we propose a non-linear statistical estimator for adaptive reconstruction of compressed ViSAR data. Our proposed reconstruction filter is based on an adaptively generated non-linear weight mask of spatial observations. It can strongly outperform several conventional and well-known reconstruction filters for three different video samples.
Highlights
The interpolation process is one of the most common processes in remote sensing image and video analysis
For instance in [1], a modified scheme was proposed for converting standard-definition television (SDTV) frames to high-definition television (HDTV) standard [2] to be used in video transmission technologies such as DVB-T
5 Conclusions In the recent years, data processing for Internet of Things (IoT) became an interesting topic of research [27–30]
Summary
The interpolation process is one of the most common processes in remote sensing image and video analysis. We want to propose a new edge-guided interpolator based on statistical estimation. In [7], a basic edge-guided interpolation based on linear minimum mean square error estimation (LMMSE) was introduced for benchmark images such that some evaluations about it have been done in [10].
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