We aim to provide a general framework of for computational photography that recovers the real scene from imperfect images, via the Deep Nonparametric Convexified Filtering (DNCF). It is consists of a nonparametric deep network to resemble the physical equations behind the image formation, such as denoising, super-resolution, inpainting, and flash. DNCF has no parameterization dependent on training data, therefore has a strong generalization and robustness to adversarial image manipulation. During inference, we also encourage the network parameters to be nonnegative and create a bi-convex function on the input and parameters, and this adapts to second-order optimization algorithms with insufficient running time, having 10X acceleration over Deep Image Prior. With these tools, we empirically verify its capability to defend image classification deep networks against adversary attack algorithms in real-time.

翻译：我们旨在通过深度非参数凸化滤波（DNCF）提供一个通用的计算摄影框架，用于从不完美图像中恢复真实场景。该框架包含一个非参数深度网络，以模拟图像形成背后的物理方程，例如去噪、超分辨率、修复和闪光处理。DNCF没有依赖训练数据的参数化，因此对对抗性图像操作具有较强的泛化能力和鲁棒性。在推理过程中，我们还鼓励网络参数为非负，并构建一个关于输入和参数的二元凸函数，这使网络能够适应运行时间不足的二阶优化算法，其速度相比深度图像先验方法提升10倍。利用这些工具，我们通过实验验证了该框架实时防御图像分类深度网络免受对抗攻击算法的能力。