Super-resolution infrared imaging via multi-receptive field information distillation network

We propose a super-resolution (SR) infrared imaging method with a multi-receptive field information distillation network. We develop a parallel progressive feature purification model to optimize the feature extraction progress and retain feature in each dimension. We use the dilation convolution to enlarge the network's receptive field and keep the number of parameters steady. We reconstruct a SR infrared image by a sub-pixel method. A series of experiments are implemented. The imaging performance of the proposed method is validated by comparing with the results from classical interpolate Bicubic, and deep learning methods VDSR, SRResNet and IMDN. Experimental results suggest that the proposed method performs favorably against the four state-of-the-art SR algorithms in visual quality. The proposed system can realize high quality image reconstruction and 2-scale SR, and requires much less images numbers for training.

Automated summary

A super-resolution infrared imaging method is proposed with a multi-receptive field information distillation network, optimizing feature extraction progress and achieving high-quality image reconstruction and 2x super-resolution. The method outperforms four state-of-the-art SR algorithms in visual quality with less training images required.