Dual-Domain Self-Supervised Learning and Model Adaption for Deep Compressive Imaging

Abstract

Deep learning has been one promising tool for compressive imaging whose task is to reconstruct latent images from their compressive measurements. Aiming at addressing the limitations of supervised deep learning-based methods caused by their prerequisite on the ground truths of latent images, this paper proposes an unsupervised approach that trains a deep image reconstruction model using only a set of compressive measurements. The training is self-supervised in the domain of measurements and the domain of images, using a double-head noise-injected loss with a sign-flipping-based noise generator. In addition, the proposed scheme can also be used for efficiently adapting a trained model to a test sample for further improvement, with much less overhead than existing internal learning methods. Extensive experiments show that the proposed approach provides noticeable performance gain over existing unsupervised methods and competes well against the supervised ones.