Ghost Imaging Denoising Based on Mean Filtering

d This paper proposes a computational ghost imaging (CGI) denoising method based on mean filtering to reduce noise interference from complex environments and improve the imaging quality of CGI. With a 3x3 template mean filter as an example, the paper designs nine groups of Hadamard shifted speckles related to the mean filter, illuminates the measured object by these shifted speckles successively, and obtains corresponding results by a bucket detector. After performing a second- order correlation on the speckles and the sum of the nine groups of values by the bucket detector, the denoised image of the measured object can be obtained. The simulation and experimental results show that compared with traditional CGI, the proposed method has better performance in improving the imaging quality under the same Gaussian and salt and pepper noises. Furthermore, it has a positive denoising effect and can be well applied in varying complex environments. In addition, the proposed method introduces the concept of mean filtering in image denoising to ghost imaging and provides a new idea for applying the signal processing method in CGI.