Title: Lottery Image Prior. Abstract: Deep Neural Networks (DNNs), either pre-trained (e.g., GAN generator) or untrained (e.g., deep image prior), could act as overparameterized image priors that help solve various image inverse problems. Since traditional image priors have much fewer parameters, those DNN-based priors naturally invite the curious question: do they really have to be heavily parameterized? Drawing inspirations from the recently prosperous research on lottery ticket hypothesis (LTH), we conjecture and study a novel “lottery image prior” (LIP), stated as: given an (untrained or trained) DNN-based image prior, it will have a sparse subnetwork that can be training in isolation, to match the original DNN’s performance when being applied as a prior to various image inverse problems. We conduct extensive experiments in two representative settings: (i) image restoration with the deep image prior, using an untrained DNN; and (ii) compressive sensing image reconstruction, using a pre-trained GAN generator. Our results validate the prevailing existence of LIP, and that it can be found by iterative magnitude pruning (IMP) with surrogate tasks. Specifically, we can successfully locate the LIP subnetworks at the sparsity range of 20%-86.58% in setting i; and those at sparsity range of 5%-36% in setting ii. Those LIP subnetworks also possess high transferrability. To our best knowledge, this is the first time that LTH is demonstrated to be relevant in the context of inverse problems or image priors, and such compact DNN-based priors may potentially contribute to practical efficiency. Code will be publicly available.