A Combined Stripe Noise Removal and Deblurring Recovering Method for Thermal Infrared Remote Sensing Images

Remote sensing images (RSIs) have been applied to many fields such as environmental monitoring, urban planning, and military defense. All-day thermal infrared imaging observation system can finely portray the trajectory of human activities and provide data support for United Nations sustainable development planning. However, in the process of RSIs, image quality degradation containing image blurring is caused by optical system aberration, satellite platform vibration, imaging system out-of-focus, and atmospheric turbulence. Moreover, stripe noise in the image is produced by the non-uniformity of infrared sensors. Image deblurring and destriping are classical tasks in RSIs, but the above two problems are discussed separately in almost all research. i.e., denoising after adding stripe noise on clear images or deblurring under the assumption that only random noise exists. In this paper, stripe noise and blur are jointly removed from on-orbit RSIs acquired by the thermal infrared spectrometer on the SDGSAT-1 satellite, using a method based on stripe component residuals and gradient property. According to the experimental results, the performance of the proposed method is greatly improved compared with processing these two tasks separately, which can provide a valuable reference for the study of high-resolution thermal infrared RSIs recovery.