Convolutional neural networks (CNNs) have found extensive application in computer-generated holography (CGH). Nonetheless, CNNs possess limited capability to effectively model intricate geometric transformations between object points and their corresponding point spread functions due to the constrained structures of fixed convolutional kernels. In order to address this issue, we propose deformable holography (DeH) algorithm for CGH. We demonstrate that utilizing deformable convolutions enable adaptive modeling of geometric transformations. The proposed DeH algorithm generates high-quality 1080P 3D holograms in real-time, consistently outperforming existing approaches. We also validate our approach on an experimental prototype holographic display, and demonstrate DeH algorithm’s ability to accurately reconstruct 3D scenes. Overall, our work introduces new possibilities of utilizing deformable convolutions for deep learning in the realm of holographic displays.
Conventionally, a digital micromirror device (DMD) can only perform binary amplitude modulation of a light field. Simultaneous amplitude and phase modulation with a DMD is achieved by our proposed error diffusion scheme with a 4f double-lens setup for the first time. The DMD pixels are encoded by adaptive global optimization of binarization errors. In holographic projection, the object image can be optically reconstructed from a complex-amplitude hologram with this scheme. Experimental results show that our proposed error diffusion scheme significantly outperforms the previous superpixel scheme in terms of the image quality and light efficiency of holographic reconstruction results.
A common phase-type spatial light modulator (SLM) can only modulate the phase part of a complex-amplitude hologram calculated from an object image. To calculate a phase-only hologram, iterative methods such as the Gerchberg–Saxton algorithm can be employed. However, one-step non-iterative phase-only hologram calculation is more favorable for real-time applications. This paper proposes a novel scheme to optimize a shared phase mask from a set of training images. Then a phase-only hologram can be simply calculated by phase truncation after any given object image similar to the training samples is multiplied with the shared mask and Fresnel diffracted. The speckle noise in the reconstructed images can be significantly suppressed if our optimized phase mask is used instead of a conventional random phase mask.
A gray-level intensity image can be employed as a host image for hiding a watermark image to protect information security. Past research works demonstrate that a gray-level hidden image can be embedded into the host image by a digital phase-only holography method. However, the fidelity of retrieved watermark image from the host image is not very satisfactory and the host image quality is degraded due to the insertion of external data bits, especially when observers focus on the saliency regions in the host image. To address this problem, we propose a steganography method based on digital holography and the saliency map of the host image. First, we calculate the hidden capacity (number of bits to be replaced) for each host image pixel based on the weighted sum of pixel intensity and saliency value. Next, a multilevel phase-only digital hologram of the watermark image will be calculated by the Gerchberg–Saxton method under the constraint of the hidden capacity of host image. Finally, we embedded the multilevel phase-only digital hologram into the host image by replacing a corresponding number of bits in each pixel. In this way, the host image can preserve good image fidelity for its saliency regions even if we hide a large amount of digital hologram data into the host image. The experimental results show that the quality of retrieved watermark image from the host image and the quality of saliency regions in the watermarked host image, in our proposed scheme, are superior to the state-of-the-art works reported.
In single-pixel imaging (SPI), the target object is illuminated with varying patterns sequentially and an intensity sequence is recorded by a single-pixel detector without spatial resolution. A high quality object image can only be computationally reconstructed after a large number of illuminations, with disadvantages of long imaging time and high cost. Conventionally, object classification is performed after a reconstructed object image with good fidelity is available. In this paper, we propose to classify the target object with a small number of illuminations in a fast manner for Fourier SPI. A naive Bayes classifier is employed to classify the target objects based on the single-pixel intensity sequence without any image reconstruction and each sequence element is regarded as an object feature in the classifier. Simulation results demonstrate our proposed scheme can classify the number digit object images with high accuracy (e.g. 80% accuracy using only 13 illuminations, at a sampling ratio of 0.3%).
In this paper, two novel hologram image processing issues, i.e., hologram decomposition and hologram inpainting, are briefly reviewed and discussed. By hologram decomposition, one hologram can be decomposed into several subholograms and each sub-hologram represents one individual item in the 3D object scene. A Virtual Diffraction Plane based hologram decomposition scheme is proposed based on Otsu thresholding segmentation, morphological dilation and sequential scan labelling. Hologram decomposition can be employed for focus distance detection in blind hologram reconstruction. By hologram impainting, a damaged hologram can be restored by filling in the missing pixels. An exemplar and search based technique is applied for hologram inpainting with enhanced computing speed by Artificial Bee Colony algorithm. Potential applications of hologram inpainting are discussed.
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