Research on Super-Resolution Image Reconstruction Based on Low-Resolution Infrared Sensor

Abstract

Due to the limitation of the pixel size in the focal plane, the low-resolution(LR) infrared sensor has a very low image resolution when sampling and imaging scenes with slightly rich spatial frequencies, and aliasing is sometimes very serious. This paper uses a new technique base on sub-pixel displacement to reconstruct high-resolution(HR) images,with reduced aliasing, from a sequence of under-sampled rotated frames of the same object. First, this paper presents an image degradation model, based on the under-sampling model of the infrared image and the infrared radiation distribution on the focal plane. Second, an image reconstruction algorithm based on image micro rotation is proposed and implemented to solve the problems of inaccurate temperature measurement and target recognition caused by low resolution. Finally, the experiments results are provided to test our algorithm, and we can obtain the image whose resolution is four or five times higher than the under-sampled frames, as well as improve the temperature measurement accuracy by more than 10%. The experimental results also show that the image reconstruction algorithm is very robust, efficient and has a good reconstruction effect.