Night Photography Rendering (NPR) poses a significant challenge due to the extreme contrast between dark and illuminated areas in scenes, stemming from concurrent capture of severely dark regions alongside intense point light sources. Existing methods, which are mainly tailored for fidelity metrics, reveal considerable perceptual gaps and often detract from visual quality. We introduce pHVI-ISPNet, a novel RAW-to-RGB framework built on the robust HVI color space. Our network integrates four distinct key refinements: RAW-domain feature processing and Wavelet-based feature propagation to mitigate high-frequency detail loss; sample-based dynamic loss coefficients to ensure stable learning across varying exposure levels; and loss term based on feature distributions to maintain rigorous color constancy. Evaluations on the dataset introduced in the NTIRE 2025 challenge on NPR confirm our approach achieves competitive fidelity while establishing new state-of-the-art results in both CIE2000 color difference and LPIPS. This validates our perceptually-driven design for high-quality nighttime imaging.

翻译：夜间摄影渲染（NPR）因场景中黑暗区域与强点光源极端对比带来的挑战而备受关注，这些场景同时包含严重欠曝区域与高强度点光源。现有方法主要针对保真度指标设计，揭示了显著的感知差距，且常损害视觉质量。我们提出pHVI-ISPNet，一种基于稳健HVI色彩空间的RAW到RGB新型框架。该网络整合了四项关键改进：RAW域特征处理及小波基特征传播以缓解高频细节损失；基于样本的动态损失系数确保不同曝光水平下的稳定学习；以及基于特征分布的损失项以维持严格的色彩恒常性。在NTIRE 2025夜间摄影渲染挑战赛引入的数据集上进行的评估证实，我们的方法在实现竞争性保真度的同时，在CIE2000色差和LPIPS指标上均创下新的最优结果。这验证了我们面向感知的高质量夜间成像设计。