High-Quality Diffractive Lens Infrared Computational Imaging via Severe Degradation Estimation Framework

Diffractive lens (DLs) computational imaging promises lightweight infrared imaging. However, it is still challenging to accurately obtain the degradation of DLs computational imaging systems to achieve high-quality imaging. Degradation estimation methods based on image-to-image are the most feasible way, but existing methods face the problem of low accuracy for severe degradation estimation. In this paper, we proposed a severe degradation estimation framework based on KernelNet, and realized the improvement of the accuracy of severe degenerate estimation by more than 1.13 dB. Furthermore, DyUnet, an image restoration network, was proposed, which is based on dynamic convolution. The results of the mid-wave diffractive lens infrared computational imaging system indicated that our method achieved high-quality imaging results comparable to the conventional one, while the number of optical elements was reduced from 7 to 2, and the weight was reduced by 50$\%$.