Fig. 5.

This figure compares the optimization landscape of the MoDL (K=1) and UNET architecture for 1-D multichannel data. These plots show the mean squared error (MSE×1000) between the reconstructions and the corresponding original images. The n1 and n2 axes represent continuous valued sampling locations around the ones marked on the mask. (a) and (b) show the landscape plot for MoDL architecture trained with a single sampling pattern and multiple sampling patterns, respectively. Similarly (c) and (d) shows corresponding plots for the UNET architecture. These plots (a)-(d) are plotted at high-frequency values around locations 6 and 15, as marked with green in the mask. Similarly, (e)-(h) show landscape plots at relatively low frequencies around locations 135 and 167. From this controlled experiment, we observe that MoDL results in a smoother landscape as compared to UNET both at low and high frequencies. In addition, the UNET landscapes become comparatively smoother with the sampling pattern augmentation strategy, which makes the approach relatively insensitive to small differences in sampling pattern, as seen from (c) to (d) and (e) to (f).