Fast Thermal Infrared Image Restoration Method Based on On-Orbit Invariant Modulation Transfer Function

Although the thermal infrared remote sensing camera plays a pivotal role in Earth observation, and impacts the target detection, surface temperature inversion, and subsequent space missions significantly, the imaging quality of the camera is constrained by its optics, image sensors, and electronics during on-orbit operation. At the same time, the traditional blind recovery algorithms, which require extensive time for estimating intricate blur kernels, encounter challenges due to varying atmospheric conditions and other factors leading to dissimilar blur kernels across different observation scenes. In this context, this article introduces a rapid image recovery algorithm rooted in the concept of the invariant modulation transfer function (IMTF) specific to on-orbit cameras. The IMTF model remains stable and impervious to influences stemming from ground targets, atmospheric conditions, and orbital or environmental fluctuations, contingent upon the camera's inherent characteristics. The extraction of the IMTF involves subjecting the transfer function's region to a modified edge methodology, followed by image recovery through a hyper-Laplacian prior inverse convolution approach. The resolution of the inverse problem is achieved by employing an alternating minimization scheme. This method addresses the mitigation of imaging artifacts originating from the camera's limitations. Comparative analysis against the state-of-the-art image recovery techniques establishes the competitiveness of the method proposed in this article, both in terms of recovery efficacy and operational efficiency. Substantiating this, experimental validation using in-orbit thermal infrared remote sensing images reveals a notable improvement in the average gradient (AG) (by a factor of 3.2), edge intensity (EI) (by a factor of 2.5), and modulation transfer function (by a factor of 1.3) of the restored images. Consequently, this approach introduces a novel perspective for enhancing the restoration of in-orbit remote sensing images.