Methods that embed data into printed images and retrieve data from printed images captured using the camera of a mobile device have been proposed. Evaluating these methods requires printing and capturing actual embedded images, which is burdensome. In this paper, we propose a method for reducing the workload for evaluating the performance of data embedding algorithms by simulating the degradation caused by printing and capturing images using generative adversarial networks. The proposed method can represent various captured conditions. Experimental results demonstrate that the proposed method achieves the same accuracy as detecting embedded data under actual conditions.

This paper presents a theoretically best algorithm within the framework of our image noise model for reconstructing 3-D from two views when all the feature points are on a planar surface. Pointing out that statistical bias is introduced if the least-squares scheme is used in the presence of image noise, we propose a scheme called renormalization, which automatically removes statistical bias. We also present an optimal correction scheme for canceling the effect of image noise in individual feature points. Finally, we show numerical simulation and confirm the effectiveness of our method.