Curved hologram generation method for speckle noise suppression based on the stochastic gradient descent algorithm

Wang, Di; Li, Nannan; Li, Yilong; Zheng, Yao; Wang, Qiong‐Hua

doi:10.1364/oe.444321

Cited by 16 publications

(6 citation statements)

References 30 publications

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“…In such a framework, the object constraint is imposed through the minimization problem itself, while the hologram constraints are applied using the partial derivative , which is calculated in each update of optimization. Although we write in a general form of stochastic scalar function with respect to the reconstructed intensity I ( ϕ ) and the object intensity I obj , the choice of is actually of great diversity for hologram synthesis 135 , 136 . In many CGH implementations, is composed of a sum of subfunctions evaluating reconstructing errors 137 , and a normalization term is occasionally added to balance other reconstruction parameters 132 , 138 .…”

Section: Frameworkmentioning

confidence: 99%

“…This is due to the fact that the superposition itself does not break any constraints on CHs, while the hologram intensity constraint ④ for POHs can be violated after superposition 171 – 173 . To enable a better generation of POHs with the superposition method, a middle complex-amplitude plane is usually added to receive the superposition of waves from multiple depths 136 , 174 , 175 . Superposition optimization enables the preservation of full-depth cues of the object wave and possesses a competitive edge in applications like 3D display 176 .…”

Section: Frameworkmentioning

confidence: 99%

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Non-convex optimization for inverse problem solving in computer-generated holography

Sui,

He,

Chu

et al. 2024

Light Sci Appl

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Computer-generated holography is a promising technique that modulates user-defined wavefronts with digital holograms. Computing appropriate holograms with faithful reconstructions is not only a problem closely related to the fundamental basis of holography but also a long-standing challenge for researchers in general fields of optics. Finding the exact solution of a desired hologram to reconstruct an accurate target object constitutes an ill-posed inverse problem. The general practice of single-diffraction computation for synthesizing holograms can only provide an approximate answer, which is subject to limitations in numerical implementation. Various non-convex optimization algorithms are thus designed to seek an optimal solution by introducing different constraints, frameworks, and initializations. Herein, we overview the optimization algorithms applied to computer-generated holography, incorporating principles of hologram synthesis based on alternative projections and gradient descent methods. This is aimed to provide an underlying basis for optimized hologram generation, as well as insights into the cutting-edge developments of this rapidly evolving field for potential applications in virtual reality, augmented reality, head-up display, data encryption, laser fabrication, and metasurface design.

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Section: Frameworkmentioning

confidence: 99%

Section: Frameworkmentioning

confidence: 99%

Non-convex optimization for inverse problem solving in computer-generated holography

Sui,

He,

Chu

et al. 2024

Light Sci Appl

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“…The main problems of digital holography include the presence of zero-order twin images [22][23][24] and optical noises [1,[25][26][27] in reconstructed images. These factors can lead to a decrease in the quality of reconstruction and, therefore, the accuracy of the obtained object images.…”

Section: Introductionmentioning

confidence: 99%

HoloForkNet: Digital Hologram Reconstruction via Multibranch Neural Network

et al. 2023

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Reconstruction of 3D scenes from digital holograms is an important task in different areas of science, such as biology, medicine, ecology, etc. A lot of parameters, such as the object’s shape, number, position, rate and density, can be extracted. However, reconstruction of off-axis and especially inline holograms can be challenging due to the presence of optical noise, zero-order image and twin image. We have used a deep-multibranch neural network model, which we call HoloForkNet, to reconstruct different 2D sections of a 3D scene from a single inline hologram. This paper describes the proposed method and analyzes its performance for different types of objects. Both computer-generated and optically registered digital holograms with resolutions up to 2048 × 2048 pixels were reconstructed. High-quality image reconstruction for scenes consisting of up to eight planes was achieved. The average structural similarity index (SSIM) for 3D test scenes with eight object planes was 0.94. The HoloForkNet can be used to reconstruct 3D scenes consisting of micro- and macro-objects.

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“…To address these problems, researchers have conducted a lot of innovative and optimized research in optical systems [ 16 , 17 , 18 ], materials [ 19 , 20 ], and algorithms [ 21 , 22 ]. However, the high-quality holographic 3D display based on the point source model still cannot be achieved with high efficiency.…”

Section: Introductionmentioning

confidence: 99%

Fast Hologram Calculation Method Based on Wavefront Precise Diffraction

Wang,

Li,

Tang

et al. 2023

Micromachines

Self Cite

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In this paper, a fast hologram calculation method based on wavefront precise diffraction is proposed. By analyzing the diffraction characteristics of the object point on the 3D object, the effective viewing area of the reproduced image is analyzed. Based on the effective viewing area, the effective hologram size of the object point is obtained, and then the accurate diffraction calculation from the object point to the wavefront recording plane (WRP) is performed. By calculating all the object points on the recorded object, the optimized WRP of the whole 3D object can be obtained. The final hologram is obtained by calculating the diffraction light field from the WRP to the holographic plane. Compared with the traditional method, the proposed method can improve the calculation speed by more than 55%, while the image quality of the holographic 3D display is not affected. The proposed calculation method provides an idea for fast calculation of holograms and is expected to contribute to the development of dynamic holographic displays.

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Curved hologram generation method for speckle noise suppression based on the stochastic gradient descent algorithm

Cited by 16 publications

References 30 publications

Non-convex optimization for inverse problem solving in computer-generated holography

Non-convex optimization for inverse problem solving in computer-generated holography

HoloForkNet: Digital Hologram Reconstruction via Multibranch Neural Network

Fast Hologram Calculation Method Based on Wavefront Precise Diffraction

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