Computational ghost imaging via adaptive deep dictionary learning

Ghost imaging has gone through from quantum to classical pseudothermal to computational field over the last two decades. As a kernel part in computational ghost imaging (CGI), the reconstruction algorithm plays a decisive role in imaging quality and system practicality. In order to introduce more prior knowledge into the reconstruction algorithm, existing research adds image patch prior into CGI and improves the imaging efficiency. In this paper, the total variation minimization algorithm via adaptive deep dictionary learning (TVADDL) is proposed to update an adaptive deep dictionary through the CGI reconstruction process. The proposed algorithm framework is able to capture more precise texture features with a multi-layer architecture dictionary and adapt the learned dictionary by gradient descent on CGI reconstruction loss value. The results of simulation and experiment show that TVADDL can achieve higher peak signal-to-noise ratio than the algorithms without patch prior and the algorithms using the shallow dictionary or non-adaptive deep dictionary.