Double filter iterative ghost imaging for high quality edge and image acquisition

Abstract

Improving imaging quality and reducing time consumption are the key problems that need to be solved in the practical application of ghost imaging. Hence, we demonstrate a double filter iterative ghost imaging method, which adopts the joint iteration of projected Landweber iterative regularization and double filtering based on block matching three dimensional filtering and guided filtering to achieve high-quality image reconstruction under low measurement and low iteration times. This method combines the advantages of ill-posed problem solution of projected Landweber iterative regularization with double filtering joint iterative de-noising and edge preservation. The numerical simulation results show that our method outperforms the comparison method by 4 to 6 dB in terms of peak signal-to-noise ratio for complex binary target ‘rice’ and grayscale target ‘aircraft’ after 1500 measurements. The comparison results of experiments and numerical simulations using similar aircraft targets show that this method is superior to the comparison method, especially in terms of richer and more accurate edge detection results. This method can simultaneously obtain high quality reconstructed image and edge feature information under low measurement and iteration times, which is of great value for the practical application fields of imaging and edge detection at the same time, such as intelligent driving, remote sensing and other fields.