Research On Band Expansion Method For Infrared Image Based On Real Image

Infrared radiation can be divided into several different bands according to the wavelength.Because of the characteristics of infrared radiation and imaging detector,infrared image can only output one specific band image at a time.Therefore,it is of research value to expand one band image to other bands image through band expansion for the development of various infrared equipment,performance evaluation and improvement.This paper focuses on how to use real infrared image to expand and transform other band images,it mainly contains these works:(1)Starting from the principle of infrared imaging,this paper analyses the whole process of infrared radiation transferring to the final image.Based on the principle of infrared imaging,this paper expounds the process of infrared imaging,which mainly includes three parts: the correlation law of infrared radiation of target,the characteristic analysis of infrared radiation in atmospheric transmission,and the effect analysis of detector system after infrared radiation is received by detector.(2)The process of band expansion by inversion-reconstruction is studied.First,the sensor effect and atmospheric effect are removed in turn by the reverse process of imaging process,then the intrinsic temperature of the target is deduced.Then the forward process of imaging is simulated by using the obtained temperature image.The infrared image of the scene in another band is obtained by using the inversion temperature result.The band expansion based on the physical process has high reliability.(3)Aiming at the shortcomings of the physical based inversion-simulation band expansion process,such as the large requirement of parameters and the tedious process,combined with the popular generation adversarial networks in the fields of image style conversion and image generation in recent years,try to figure out whether deep learning can be applied in infrared image band extension or not.First,aiming at the characteristics that the cycle generation adversarial networks can accept the unpaired image datasets,training the network with the infrared images of different bands.After training,get an end-to-end network model which can be used for image conversion of different bands,and the test set is used to test it.Finally,evaluate the results of transformed by the model with various image evaluation indicators,analyze the quality of the results obtained by deep learning for band expansion.