Publications

Wu, XB; Zheng, LL; Liu, CY; Gao, T (2024). Hardware Platform Stripe Noise Estimation and Random Noise Removal Framework. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 62, 5644013.

Abstract
When the space remote sensing camera is imaging in orbit, the image data usually contain obvious stripe noise and random noise due to the influence of imaging mode (push-broom mode), imaging environment, and other factors. Previous research has primarily concentrated on postprocessing the image using the substantial computational resources of the computer platform, with little consideration given to the feasibility of real-time processing on the satellite platform. In light of the above, a novel hardware platform stripe noise estimation and random noise removal framework (HSENRF) is proposed for the first time, with the objective of enabling real-time simultaneous destriping and denoising on the satellite platform. It employs the theory of maximum likelihood estimation (MLE) to construct the image processing module (IMM) to remove the stripe noise as well as the random noise through the application of the modified nonlocal mean algorithm (M-NLM) as well as the expectation maximization (EM) algorithm. Multiple IMM modules are arranged in the form of a pipeline to achieve multiple iterations of image data. The framework has been successfully deployed on the Xilinx xc7k410tffg676 FPGA platform, and a large number of simulations and experiments show that it has high computational efficiency and good performance. At a system clock of 50 MHz, it achieves a frame rate of 162.77 frames/s when processing 8-bit 512 x 512 image. In addition, it also has strong robustness and can effectively remove noise components in remote sensing images with different scenes and different spatial resolutions.

DOI:
10.1109/TGRS.2024.3474925

ISSN:
0196-2892