Zero-Order Reverse Filtering

Xin Tao, Chao Zhou, Xiaoyong Shen, Jue Wang, Jiaya Jia; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 222-230

In this paper, we study an unconventional but practically meaningful reversibility problem of commonly used image filters. We broadly define filters as operations to smooth images or to produce layers via global or local algorithms. And we raise the intriguingly problem if they are reservable to the status before filtering. To answer it, we present a novel strategy to understand general filter via contraction mappings on a metric space. A very simple yet effective zero-order algorithm is proposed. It is able to practically reverse most filters with low computational cost. We present quite a few experiments in the paper and supplementary file to thoroughly verify its performance. This method can also be generalized to solve other inverse problems and enables new applications.