An autoencoder based formulation for compressed sensing reconstruction

This work proposes a new formulation for image reconstruction based on the autoencoder framework. The work follows the adaptive approach used in prior dictionary and transform learning based reconstruction techniques. Existing autoencoder based reconstructions are non-adaptive; they are trained on a separate training set and applied on another. In this work, the autoencoder is learnt from the patches of the image it is reconstructing. Experimental studies on MRI reconstruction shows that the proposed method outperforms state-of-the-art methods in dictionary learning, transform learning and (non-adaptive) autoencoder based approaches.