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

Source: [Deep Nonparametric Convexified Filtering for Computational Photography, Image Synthesis and Adversarial Defense](<https://www.sota2.com/research/paper/68fa5adc1e209131ce9c3438>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T16:43:44.803Z

Authors: Jianqiao Wangni

Published: 2023-09-13T04:57:12.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2309.06724>)

[PDF](<https://arxiv.org/pdf/2309.06724.pdf>)