Coded aperture compression temporal imaging based on a dual-mask and deep denoiser

Abstract

Coded aperture compressive temporal imaging (CACTI) is the mapping of multiple frames using different encoding patterns into a single measurement and then using an algorithm to reconstruct the required high-dimensional signals, thus enabling high-speed photography on low-speed cameras. An encoding pattern and a reconstruction algorithm both play a critical role for CACTI. To improve the quality of the reconstruction, in terms of encoding, we took advantage of the reflective properties of the digital micromirror device and used a complementary dual-mask pattern to obtain more projection information. In terms of decoding, we developed what we believe, to the best of our knowledge, is a new model combining the weighted Landweber regularization with the relaxation strategy and a deep denoiser. The experimental results show the superiority of our proposed encoding–decoding combination, which achieves better performance in terms of the peak SNR, structural similarity index measure, and visual effects.