Rapid super resolution for infrared imagery.

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Abstract

Infrared (IR) imagery is used in agriculture for irrigation monitoring and early detection of disease in plants. The common IR cameras in this field typically have low resolution. This work offers a method to obtain the super-resolution of IR images from low-power devices to enhance plant traits. The method is based on deep learning (DL). Most calculations are done in the low-resolution domain. The results of each layer are aggregated together to allow a better flow of information through the network. This work shows that good results can be achieved using depthwise separable convolution with roughly 300K multiply-accumulate computations (MACs), while state-of-the-art convolutional neural network-based super-resolution algorithms are performed with around 1500K MACs. MTF analysis of the proposed method shows a real ×4 improvement in the spatial resolution of the system, out-preforming the diffraction limit. The method is demonstrated on real agricultural images.