Pro-Mist filters are widely used in cinematography for their ability to create soft halation, lower contrast, and produce a distinctive, atmospheric style. These effects are difficult to reproduce digitally due to the complex behavior of light diffusion. We present ProMist-5K, a dataset designed to support cinematic style emulation. It is built using a physically inspired pipeline in a scene-referred linear space and includes 20,000 high-resolution image pairs across four configurations, covering two filter densities (1/2 and 1/8) and two focal lengths (20mm and 50mm). Unlike general style datasets, ProMist-5K focuses on realistic glow and highlight diffusion effects. Multiple blur layers and carefully tuned weighting are used to model the varying intensity and spread of optical diffusion. The dataset provides a consistent and controllable target domain that supports various image translation models and learning paradigms. Experiments show that the dataset works well across different training settings and helps capture both subtle and strong cinematic appearances. ProMist-5K offers a practical and physically grounded resource for film-inspired image transformation, bridging the gap between digital flexibility and traditional lens aesthetics. The dataset is available at https://www.kaggle.com/datasets/yingtielei/promist5k.

翻译：Pro-Mist滤镜在电影摄影中广泛应用，因其能够产生柔和的光晕、降低对比度并营造独特的氛围风格。由于光扩散行为的复杂性，这些效果难以通过数字方式精确复现。本文提出了ProMist-5K数据集，旨在支持电影风格的数字模拟。该数据集采用基于物理启发的流程在场景参考线性空间中构建，包含20,000对高分辨率图像，涵盖四种配置组合：两种滤镜密度（1/2与1/8）和两种焦距（20mm与50mm）。与通用风格数据集不同，ProMist-5K专注于真实的光晕与高光扩散效果。通过采用多层模糊处理及精细调校的权重分配，该数据集模拟了光学扩散在强度与范围上的动态变化。本数据集提供了一个一致且可控的目标域，可支持多种图像转换模型与学习范式。实验表明，该数据集在不同训练设置下均表现良好，有助于捕捉从细微到强烈的电影视觉特征。ProMist-5K为受电影启发的图像变换提供了实用且基于物理原理的资源，弥合了数字处理灵活性与传统镜头美学之间的鸿沟。数据集可通过 https://www.kaggle.com/datasets/yingtielei/promist5k 获取。