Representing visual signals with implicit coordinate-based neural networks, as an effective replacement of the traditional discrete signal representation, has gained considerable popularity in computer vision and graphics. In contrast to existing implicit neural representations which focus on modelling the scene only, this paper proposes a novel implicit camera model which represents the physical imaging process of a camera as a deep neural network. We demonstrate the power of this new implicit camera model on two inverse imaging tasks: i) generating all-in-focus photos, and ii) HDR imaging. Specifically, we devise an implicit blur generator and an implicit tone mapper to model the aperture and exposure of the camera's imaging process, respectively. Our implicit camera model is jointly learned together with implicit scene models under multi-focus stack and multi-exposure bracket supervision. We have demonstrated the effectiveness of our new model on a large number of test images and videos, producing accurate and visually appealing all-in-focus and high dynamic range images. In principle, our new implicit neural camera model has the potential to benefit a wide array of other inverse imaging tasks.

翻译：以隐式坐标神经网络表示视觉信号，作为传统离散信号表示的有效替代，已在计算机视觉和图形学领域获得广泛关注。针对现有隐式神经表示仅聚焦于场景建模的局限，本文提出一种新颖的隐式相机模型，将相机的物理成像过程表征为深度神经网络。我们通过两项逆向成像任务验证该新型隐式相机模型的能力：i) 生成全聚焦照片，ii) 高动态范围成像。具体而言，我们设计了隐式模糊生成器与隐式色调映射器，分别模拟相机成像过程中的光圈与曝光。在多焦点堆栈与多曝光包围监督下，该隐式相机模型与隐式场景模型实现联合学习。大量测试图像与视频的实验结果表明，本方法能够生成精确且视觉自然的全聚焦图像及高动态范围图像。从原理上看，该新型隐式神经相机模型有望惠及更广泛的逆向成像任务。