Ill-posed inverse problems embodied in parallel imaging remain an active research topic in several decades, with new approaches constantly emerging. Built on the observation that both dictionary learning and conventional sparse coding extract high-frequency component to model, we derived a novel strategy named HDAEP to explore the prior on high-frequency domain on the basis of denoising autoencoding. After the prior is learned from the trained network, the iteratively Gauss-Newton method is employed to jointly estimating the images and coil sensitivities. Experimental results show that the proposed method can achieve superior performances on parallel MRI reconstruction compared to state-of-the-arts.
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