ReviewLight, science & applications2024
Non-convex optimization for inverse problem solving in computer-generated holography.
Review in Light, science & applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Mesh-represented and learning-empowered hologram synthesis for full 3D holographic displays.Nature communications · 2026Article
- Waveguide-Based Four-Channel Crosstalk-Free Metahologram Multiplexed by Diffraction Orders.Nanophotonics (Berlin, Germany) · 2026Article
- Spatial light modulator via optically addressed metasurface.Nature nanotechnology · 2026Article
- Metaoptics merging computational optics and optical computing toward intelligent visual perception.Science advances · 2026Review
- Rapid in-air ultrasound holography measurement and camera-in-the-loop generation using thermography.Communications engineering · 2025Article
- High-Fidelity Computational Microscopy via Feature-Domain Phase Retrieval.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- High contrast holography through dual modulation.Scientific reports · 2025Article
- Review
- Computational Super-Resolution: An Odyssey in Harnessing Priors to Enhance Optical Microscopy Resolution.Analytical chemistry · 2025Review
- Real-time holographic camera for obtaining real 3D scene hologram.Light, science & applications · 2025Article
- Motion Hologram: Jointly optimized hologram generation and motion planning for photorealistic 3D displays via reinforcement learning.Science advances · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.