It has been a key problem in document image superresolution which the noise model is uncertain

edge and texture of characters tend to complex and changeable. Geman&McClure (G&M) norm instead of L1 and L2 norm was proposed and used to improve the robustness of the algorithm. A regularization item combining Bilateral Total Variation (BTV) and Huber function was designed with adopting Lucas-Kanade light flow registration algorithm which is highly compatible for the proposed algorithm. This algorithm uses full information of the characters structure characteristics to make the algorithm pays more attention to the edge details in the process of reconstruction

applies the edge direction information more efficiently

and promotes information fusion of a series of low resolution images. Finally

experiments corresponding to L1BTV

L2BTV and G&M BTV regularization algorithm without Huber function were carried out

the results show that the algorithm under the G&M norm with combination of BTV and Huber function is much more excellent than other algorithms. In the environment of mixed noise model

document image super-resolution reconstruction can smooth the noise significantly

sharpen the edge and improve the resolution of characters in document image. The recognition rate of characters was improved by 1469%

and the operation time of the proposed algorithm was shorten by 29.34% at the same time.