Spatial Modulation Full Polarization Computing Imaging Super-Resolution via Scene Transfer

The spatial modulation full polarization computing imaging can obtain both visible and infrared channel images synchronously.However,due to the limitation of the detector,the spatial resolution of the two channels is inconsistent,which brings inconvenience to the following image fusion and target detection proc-ess.A spatial modulation computing imaging super-resolution method via scene feature transfer learning is proposed.Firstly,the scene feature transfer model is constructed.The model is based on the analysis of the spatial modulation computing imaging characteristics of different source images in the same scene.Secondly,the convolutional neural networks(CNN)structure of scene transfer is improved and the rectified linear unit activation function is selected.At the same time,the spatial resolution consistency constraint is added.Then,the optimal spectral transfer response learning strategy is designed and added to the super-resolution network as the front-end input.Finally,the parameters of spectral transfer response optimization and full polarization super-resolution reconstruction are learned together to obtain the high-resolution polarization image.Some su-per-resolution experiments with scale factors 2 and 3 are carried out using simulation data and system data of the actual imaging system.The experiments result is evaluated by multiple indicators with three aspects such as subjective visual effect,objective quantitative index and polarization parameter analysis results.In the visual effect,the method can keep the object contour and suppress the noise interference.On the 16 objective index data,the method obtains 10 better,3 equal and 3 lower results compared with the others.The results verify the effectiveness of the method and also provide data support for calibration correction of imaging system.