A multi-scale mixed convolutional network for infrared image super-resolution reconstruction

Infrared image is widely used in military, medical, monitoring security and other fields. Due to the limitation of hardware devices, infrared image has the problems of low signal-to-noise ratio, blurred edge and low contrast. In view of the above problems, In this paper, a super-resolution reconstruction method of infrared image based on mixed convolution multi-scale residual network is proposed. Through the multi-scale residual network to improve the utilization of features, the mixed convolution is introduced into the multi-scale residual network, which can increase the receptive field without changing the size of the feature map and eliminate the blind spots. The extracted features are fused by recursive fusion to improve the utilization of features. Through experiments and tests on multiple infrared image data sets, Through the test on the infrared image data set show that the proposed method can improve the infrared image edge information, fully extract the texture details from the infrared image, and suppress noise. The objective index of the reconstructed infrared image is mainly better than that of the contrast method, and can still achieve a better reconstruction effect in the real scene.