In order to improve specific application imaging quality of infrared seeker, a model for imaging condition and application scene was constructed by using statistical image of infrared image seeker. On the one hand, L1/L2 norm was used to constrain the restored image according to the characteristics of multi-scale imaging, which kept details in the iterative restoration. On the other hand, a sparse Laplacian distribution was used to constrain fuzzy kernel, and to maintain image’s content. Image kernel size can be adjusted adaptively by calculating the image details. The result shows that the prior constrain algorithm of this paper can effectively improve the image quality. In addition, the evaluation index is improved by this prior design, the contrast enhancement coefficient index is increased by 20%～50%, the peak signal to noise ratio is increased by 0.8～3.4, and the cumulative probability of blur detection is increased by 0.3～0.5.This study is helpful for complex scene and moving vector imaging.