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.
Highlights
With the development of artificial intelligence technology and Internet of Things technology, IT has continued to penetrate into traditional industries, including home appliance industry
Starting from the angle of the radiation intensity of illumination distribution on the focal plane and the reduced sampling model of super-resolution, our experiment theoretically proves that the weighted average of high-resolution images according to the shape factor is the low-resolution image
It was confirmed through experiments that the application of this algorithm can increase the resolution of the M sensor four times, can increase the temperature measurement accuracy of the human body by 24%, and can increase the temperature measurement accuracy of the black body by 31%
Summary
With the development of artificial intelligence technology and Internet of Things technology, IT has continued to penetrate into traditional industries, including home appliance industry. In research on image super-resolution, there are generally blurs caused by the limited detector size and optics [9], which are all described by the point spread functions, from that the degradation model and many super-resolution algorithms are obtained [10], [11]. In many applications of low-resolution infrared sensors, the range of the super-resolution is greater than the resolution of the imaging system, but can not exceed the diffraction limit of the optical system to meet the requirements. At this time, the traditional super-resolution models and algorithms are no longer suitable, and their computational complexity adds the pressure of great computational cost to VOLUME 8, 2020 the application. We use the rotation method to sample at different angles through infrared sensor, and to obtain multiple frames of low-resolution images, which, to a certain extent, solves the ill-posedness problem of super-resolution
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