A super-resolution algorithm via tiny recurrent convolutional neural network was proposed on the basis of the principles of image degradation. The proposed model has very few parameters when compared to super-resolution algorithms based on naive statistical learning. Model parameters of the proposed model have their specific physical meanings because corresponding image degradation model is introduced and regularizes the proposed model implicitly. This paper also provides an inner view of the related parameters of the algorithm and how these parameters influence the performance of the algorithm. As a result, the proposed model can achieve better performance in terms of running speed and peak signal noise ratio, comparing to current iterative backprojection algorithm. The result illustrates that the proposed algorithm only takes about 75% time consumpion, but improves the peak signal noise ratio by 0.2 dB comparing to conventional backprojection algorithm and 1.2 dB improvement comparing to bilinear interpolation respectively.