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.

我们提出了一种具有多接收场信息蒸馏网络的超分辨率（SR）红外成像方法。我们开发了一个并行的渐进式特征净化模型，以优化特征提取进度并在每个维度上保留特征。我们使用膨胀卷积来扩大网络的接收范围，并保持参数数量稳定。我们通过亚像素方法重建SR红外图像。进行了一系列实验。通过与经典插值Bicubic和深度学习方法VDSR，SRResNet和IMDN的结果进行比较，验证了该方法的成像性能。实验结果表明，所提出的方法在视觉质量上优于四种最新的SR算法。