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. Author manuscript; available in PMC: 2022 Nov 1.
Published in final edited form as: Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3596–3600. doi: 10.1109/EMBC46164.2021.9630985

Fig. 1:

Fig. 1:

Schematic of the unrolled ADMM for ℓ1-wavelet compressed sensing (CS). One unrolled iteration of ADMM with ℓ1-wavelet regularizers consists of regularizer (R), data consistency (DC) and dual update (DWT: Discrete wavelet transform). In accordance with the ADMM framework, learnable parameters are shared across different unrolled iterations. In its simplest form, this leads to 3 trainable parameters per wavelet. Further enhancements, such as separate thresholds for wavelet subbands and reweighted ℓ1 minimization increase this number to 16 trainable parameters per wavelet.