Super-Resolution Reconstruction Algorithm of Target Image Based on Learning Background

In the realistic video monitoring environment, the traditional super-resolution reconstruction technique based on prior knowledge is not suitable for monitoring the super-resolution reconstruction of the image. In this paper, a super-resolution reconstruction algorithm of target image based on learning background is proposed. The first part of the algorithm is to design a non-manifolds consistency algorithm for super-resolution reconstruction of the whole video surveillance image. The second part of the algorithm, from video surveillance images in the background, to select the characteristics significantly, and the relatively fixed background. And then to study the background, study a mapping function can improve image quality. Finally, the mapping function to restoration image of interested target, so that we can better recover the structure and texture of target image details. The experimental results show that the proposed algorithm improves both the objective evaluation index and the subjective visual effect.