Deep frequency-recurrent priors for inverse imaging reconstruction

Abstract Ill-posed inverse problems in imaging remain an active research topic in several decades, with new approaches constantly emerging. Recognizing that the popular dictionary learning and convolutional sparse coding are both essentially modeling the high-frequency component of an image, which convey most of the semantic information such as texture details, in this work we propose a novel multi-profile high-frequency transform-guided denoising autoencoder for attaining deep frequency-recurrent prior (DFRP). To achieve this goal, we first extract a set of multi-profile high-frequency components via a specific transformation and add artificial Gaussian noise to these high-frequency components as training samples. Then, as the high-frequency prior information is learned, we incorporate it into classical iterative reconstruction by proximal gradient descent. Preliminary results on highly under-sampled magnetic resonance imaging and sparse-view computed tomography reconstruction demonstrated that the proposed method can efficiently reconstruct feature details and presented advantages over state-of-the-arts.