A deep learning framework is presented that transforms the image reconstruction problem from under-sampled k-space data into pixel classification. The underlying target image is represented by a quantized image, which makes it possible to design a network that classifies each pixel to a quantized level. We have compared two deep learning encoder-decoder networks with the same complexity: one is a classification network and the other is a regression network. Even though the complexity of both the networks is the same, the images reconstructed using the classifier network have resulted in a six times improvement in the mean squared error compared to the regression network.
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