CV-HoloSR: Hologram to hologram super-resolution through volume-upsampling three-dimensional scenes

2026-04-11

AI Summary

This work addresses the severe quadratic depth distortion introduced by existing holographic super-resolution methods during volumetric upsampling, which compromises three-dimensional focus accuracy. To overcome this limitation, the authors propose CV-HoloSR, a novel framework that leverages a complex-valued residual dense network and a depth-aware perceptual reconstruction loss to achieve, for the first time, physically consistent linear depth scaling in volumetric upsampling, thereby effectively recovering high-frequency interference details. The study introduces the first 4K holographic dataset tailored for large depth ranges and devises a complex-valued LoRA fine-tuning strategy that enables efficient adaptation to new depth and display configurations with only 200 samples. Experiments demonstrate a 32% improvement in LPIPS (reaching 0.2001), a 75% reduction in training time (from 22.5 to 5.2 hours), and successful generalization to unseen depth ranges and novel display setups.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Existing hologram super-resolution (HSR) methods primarily focus on angle-of-view expansion. Adapting them for volumetric spatial up-sampling introduces severe quadratic depth distortion, degrading 3D focal accuracy. We propose CV-HoloSR, a complex-valued HSR framework specifically designed to preserve physically consistent linear depth scaling during volume up-sampling. Built upon a Complex-Valued Residual Dense Network (CV-RDN) and optimized with a novel depth-aware perceptual reconstruction loss, our model effectively suppresses over-smoothing to recover sharp, high-frequency interference patterns. To support this, we introduce a comprehensive large-depth-range dataset with resolutions up to 4K. Furthermore, to overcome the inherent depth bias of pre-trained encoders when scaling to massive target volumes, we integrate a parameter-efficient fine-tuning strategy utilizing complex-valued Low-Rank Adaptation (LoRA). Extensive numerical and physical optical experiments demonstrate our method's superiority. CV-HoloSR achieves a 32% improvement in perceptual realism (LPIPS of 0.2001) over state-of-the-art baselines. Additionally, our tailored LoRA strategy requires merely 200 samples, reducing training time by over 75% (from 22.5 to 5.2 hours) while successfully adapting the pre-trained backbone to unseen depth ranges and novel display configurations.

Problem

• hologram super-resolution
• depth distortion
• volume up-sampling
• 3D focal accuracy
• spatial up-sampling

Innovation

• Complex-Valued Super-Resolution
• Volume Upsampling
• Depth-Aware Perceptual Loss
• Low-Rank Adaptation (LoRA)
• Holographic 3D Imaging