A Generalized Detail-Preserving Super-Resolution method

The Super-Resolution (SR) technology, which aims to obtain a high-resolution image by using a set of low-resolution images of the same scene, has become one of the hottest research fields. In this paper, we propose a generalized detail-preserving SR method built on a reasonable observation model and a new image prior model. In order to preserve detail information (i.e., sharp edge and texture information), many SR methods have been established by using the traditional observation models and various image prior models. However, the sensor measurement error, the model error, etc., which are not considered in the existing SR methods, also inevitably make some information get lost from the high-resolution image. In this paper, we use a reasonable observation model that describes the degradation process more fully and exactly for SR reconstruction. Also, we propose an adaptive non-local edge-preserving image prior to model the high-resolution image, which imposes a non-local smoothness constraint on the HR image. Thus, the proposed SR method can better preserve the detail information of an image while avoiding artifacts. The generalized detail-preserving SR method has been tested in artificially generated and real data. The experimental results show that the proposed method can reconstruct higher quality images in both quantitative term and perceptual effect.