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

Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense