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.

翻译：现有全息图超分辨率（HSR）方法主要聚焦于视角扩展。将其适配至体空间上采样会引入严重的二次深度失真，降低三维聚焦精度。我们提出CV-HoloSR——一种专为在体积上采样过程中保持物理一致的线性深度缩放而设计的复值HSR框架。该模型基于复值残差稠密网络构建，并采用新型深度感知感知重构损失进行优化，有效抑制过度平滑以恢复锐利的高频干涉图样。为此，我们引入了一个涵盖高达4K分辨率的大深度范围综合数据集。此外，为克服预训练编码器在扩展至大规模目标体积时固有的深度偏差，我们集成了一种利用复值低秩适配的参数高效微调策略。大量数值模拟与物理光学实验证明了本方法的优越性。CV-HoloSR在感知真实度方面较现有最优基线提升32%（LPIPS值达0.2001）。同时，我们定制的LoRA策略仅需200个样本，即能将训练时间减少75%以上（从22.5小时降至5.2小时），并成功将预训练骨干网络适配至未见深度范围及新型显示配置。