Bokeh rendering methods play a key role in creating the visually appealing, softly blurred backgrounds seen in professional photography. While recent learning-based approaches show promising results, generating realistic Bokeh with variable strength remains challenging. Existing methods require additional inputs and suffer from unrealistic Bokeh reproduction due to reliance on synthetic data. In this work, we propose Bokehlicious, a highly efficient network that provides intuitive control over Bokeh strength through an Aperture-Aware Attention mechanism, mimicking the physical lens aperture. To further address the lack of high-quality real-world data, we present RealBokeh, a novel dataset featuring 23,000 high-resolution (24-MP) images captured by professional photographers, covering diverse scenes with varied aperture and focal length settings. Evaluations on both our new RealBokeh and established Bokeh rendering benchmarks show that Bokehlicious consistently outperforms SOTA methods while significantly reducing computational cost and exhibiting strong zero-shot generalization. Our method and dataset further extend to defocus deblurring, achieving competitive results on the RealDOF benchmark. Our code and data can be found at https://github.com/TimSeizinger/Bokehlicious

翻译：散景渲染方法在创建专业摄影中视觉吸引力强、背景柔和模糊的效果方面发挥着关键作用。尽管近期基于学习的方法显示出有前景的结果，但生成具有可变强度的真实感散景仍然具有挑战性。现有方法需要额外输入，并且由于依赖合成数据而存在散景再现不真实的问题。在本工作中，我们提出了Bokehlicious，一种高效的网络，通过模拟物理镜头光圈的孔径感知注意力机制，提供对散景强度的直观控制。为进一步解决高质量真实世界数据的缺乏问题，我们提出了RealBokeh，一个新颖的数据集，包含23,000张由专业摄影师拍摄的高分辨率（24-MP）图像，涵盖具有不同光圈和焦距设置的多样化场景。在我们新的RealBokeh数据集以及已建立的散景渲染基准测试上的评估表明，Bokehlicious始终优于最先进的方法，同时显著降低了计算成本，并表现出强大的零样本泛化能力。我们的方法和数据集进一步扩展到离焦去模糊任务，在RealDOF基准测试上取得了具有竞争力的结果。我们的代码和数据可在 https://github.com/TimSeizinger/Bokehlicious 找到。