Camera motion introduces spatially varying blur due to the depth changes in the 3D world. This work investigates scene configurations where such blur is produced under parallax camera motion. We present a simple, yet accurate, Image Compositing Blur (ICB) model for depth-dependent spatially varying blur. The (forward) model produces realistic motion blur from a single image, depth map, and camera trajectory. Furthermore, we utilize the ICB model, combined with a coordinate-based MLP, to learn a sharp neural representation from the blurred input. Experimental results are reported for synthetic and real examples. The results verify that the ICB forward model is computationally efficient and produces realistic blur, despite the lack of occlusion information. Additionally, our method for restoring a sharp representation proves to be a competitive approach for the deblurring task.

翻译：相机运动因三维世界中深度变化而引入空间变化的模糊。本研究探讨了在视差相机运动下产生此类模糊的场景配置。我们提出了一种简单而精确的图像合成模糊（ICB）模型，用于处理深度相关的空间变化模糊。该（前向）模型能够从单张图像、深度图和相机轨迹中生成逼真的运动模糊。此外，我们利用ICB模型与基于坐标的多层感知机（MLP）相结合，从模糊输入中学习清晰的神经表示。实验报告涵盖了合成与真实示例。结果验证了ICB前向模型尽管缺乏遮挡信息，但仍具有计算高效性并能生成逼真的模糊效果。同时，我们恢复清晰表示的方法在去模糊任务中被证明是一种具有竞争力的方案。