Color ghost imaging based on optimized random speckles and truncated singular value decomposition

Abstract

The ambient noise always harms the image quality in ghost imaging. In this letter, we propose a scheme of color ghost imaging to reduce the noise impact. In addition to the method of truncated singular value decomposition (TSVD), the measurement matrix is optimized with low-pass filters. In experiment, four (Gaussian, averaging, circular averaging, and median) filters are applied to the patterns of random speckles, which reforms the measurement matrix. By using the TSVD method, the pseudo-inverse matrix of the optimized measurement matrix is obtained and used to reconstruct the images. With proper truncation rates, the mean square errors of the reconstructed images are greatly decreased. Therefore, the image quality in our scheme is greatly improved. Further simulation analysis shows that the point spread functions are greatly optimized after filtering the random speckles. Our technique strengthens the anti-noise ability of ghost imaging, especially in complex environment.