Multiframe infrared image super-resolution reconstruction using generative adversarial networks

Generative adversarial networks had shown promising potential in conditional image generation. It seemed that the GANs were particularly suitable for use in image super-resolution reconstruction. However, there was a shortcoming of excessive smoothness and lack of high frequency detail information for the reconstructed SR images by using GANs. Aiming at resolving the problem that the method of single image super-resolution reconstruction ignored the spatio-temporal relationship between image frames, a method of multiframe infrared image super-resolution reconstruction based on generative adversarial networks (M-GANs) was proposed in this paper. Firstly, motion compensation was proposed for registration low resolution image frames; Secondly, a weight representation convolutional layer was performed to calculate the weight transfer; Finally, the generative adversarial network was used to reconstruct the high resolution image. Experimental results demonstrate that the proposed method surpass current state-of-the-art performance of both subjective and objective evaluation.