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Deep Neural Network (DNN)-based image reconstruction, despite many successes,often exhibits uneven fidelity between high and low spatial frequency bands. Inthis paper we propose the Learning Synthesis by DNN (LS-DNN) approach where twoDNNs process the low and high spatial frequencies, respectively, and, improvingover [30], the two DNNs are trained separately and a third DNN combines theminto an image with high fidelity at all bands. We demonstrate LS-DNN in twocanonical inverse problems: super-resolution (SR) in diffraction-limitedimaging (DLI), and quantitative phase retrieval (QPR). Our results also showcomparable or improved performance over perceptual-loss based SR [21], and canbe generalized to a wider range of image recovery problems.

Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery