Ground-Truth Free Meta-Learning for Deep Compressive Sampling

Abstract

Compressive sampling (CS) is an efficient technique for imaging. This paper proposes a ground-truth (GT) free meta-learning method for CS, which leverages both ex-ternal and internal deep learning for unsupervised high-quality image reconstruction. The proposed method first trains a deep neural network (NN) via external meta-learning using only CS measurements, and then efficiently adapts the trained model to a test sample for exploiting sample-specific internal characteristic for performance gain. The meta-learning and model adaptation are built on an improved Stein's unbiased risk estimator (iSURE) that provides efficient computation and effective guidance for accurate prediction in the range space of the adjoint of the measurement matrix. To improve the learning and adaption on the null space of the measurement matrix, a modi-fied model-agnostic meta-learning scheme and a null-space consistency loss are proposed. In addition, a bias tuning scheme for unrolling NNs is introduced for further acceler-ation of model adaption. Experimental results have demonstrated that the proposed GT-free method performs well and can even compete with supervised methods.