Image transmission and denoising under non-Kolmogorov turbulence
Meiyong Xu, Dongfang Wu, Yutong Wang, Xinning Lu, Jian Wan, Kaimin Wang, Dawei Zhang · 2024
Image signal degradation of computational ghost imaging is an important issue in atmospheric turbulence, especially for the images in laboratory safety monitoring. However, the relevant research under non-Kolmogorov turbulence model has been rarely reported. To address this issue, we proposed an efficient image signal processing and transmission scheme tailored for low-sampling conditions to enhance image signal denoising for applications like educational laboratory safety monitoring. Our approach employed computational ghost imaging enhanced by a modified sequence on a Hadamard orthogonal basis. Then we simulated turbulence using a non-Kolmogorov spectrum mode, which a phase screen approach realized. We applied a bucket detector to capture light intensities reflected or scattered from the target and reconstructed the target image from captured signals with the computational ghost imaging algorithms. The orthogonally modified Hadamard matrix patterns with a compress ratio were applied to reduce noise and enhance image quality. Simulations show significant image quality improvements over traditional schemes.