Image quality enhancement of embedded holograms in holographic information hiding using deep neural networks
This work addresses a specific issue in holographic steganography and copyright protection, offering an incremental improvement for applications requiring clearer embedded holograms.
The paper tackled the problem of low visual quality in embedded holograms within holographic information hiding by using deep neural networks to restore darkened images, resulting in improved perception of the embedded hologram's reconstructed image.
Holographic information hiding is a technique for embedding holograms or images into another hologram, used for copyright protection and steganography of holograms. Using deep neural networks, we offer a way to improve the visual quality of embedded holograms. The brightness of an embedded hologram is set to a fraction of that of the host hologram, resulting in a barely damaged reconstructed image of the host hologram. However, it is difficult to perceive because the embedded hologram's reconstructed image is darker than the reconstructed host image. In this study, we use deep neural networks to restore the darkened image.