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. Author manuscript; available in PMC: 2022 Jun 20.
Published in final edited form as: Phys Med Biol. 2019 Feb 18;64(4):045018. doi: 10.1088/1361-6560/aafd50

Figure 3.

Figure 3.

CR, CNR, and speckle SNR for DNN beamformers as a function of channel SNR. (a)–(c) Out of all DNN beamformers that used training dataset #1, we selected the best DNN beamformer in terms of CNR, and display CR, CNR, and SNRs for this beamformer as the blue (circle) line. Out of all DNN beamformers that used training dataset #2, we selected the best DNN beamformer in terms of CNR, and display CR, CNR, and SNRs for this beamformer as the orange (triangle) line. Out of all DNN beamformers that used training dataset #3, we selected the best DNN beamformer in terms of CNR, and display CR, CNR, and SNRs for this beamformer as the green (square) line. Performance of the best DNN beamformer as a function of (d)–(f) batch size, (g)–(i) number of hidden layers, (j)–(l) layer width, (m)–(o) input Gaussian noise, (p)–(r) input dropout, (s)–(u) hidden layer dropout, and (v)–(x) weight decay. For comparison, the dashed black line shows the performance of DAS and the dark gray dashed dotted line shows the performance for GCF. The cyan dashed dotted line shows the theoretical limit for CNR.