It′s necessary to improve the resolut ion of infrared image because the low-resolution image cannot meet the demand of many applications.Traditional a pproaches reconstruct infrared image merely from low-resolution infrared image,which deliver limited results. High-resolution visible image,by contrast,can be easily obtained with a CCD camera and has a strong correlation with the infrared image,from which we can increase the resolution of infrared image by utilizing the informat ion of visible image.This paper presents a new infrared image resolution improvement framework based on multi-s ensor.The proposed method consists of two key points.The first one is that the correlation between infrar ed and visible images should be used efficiently;the second one is that the regularization model should be suitable for infrared image super-resolution. We use phase congruency to extract the edges of visible image,and the edges are then combined with a regularization model,which utilizes the correlation sufficiently.In addition,the regularization model is built by first-order graduate operator and total generalized variation regularization,which is applic able to the reconstruction of infrared image.Finally,this method infers the super-resolved infrared image with a first -order primal-dual optimization scheme.Experimental results demonstrate that the proposed method can obtain clea r result s and suppress noise effectively. When compared with other methods,the proposed algorithm is superior in terms of subjective and objective qualities.