  Deep learning based methods hold state-of-the-art results in image denoising,
but remain difficult to interpret due to their construction from poorly
understood building blocks such as batch-normalization, residual learning, and
feature domain processing. Unrolled optimization networks propose an
interpretable alternative to constructing deep neural networks by deriving
their architecture from classical iterative optimization methods, without use
of tricks from the standard deep learning tool-box. So far, such methods have
demonstrated performance close to that of state-of-the-art models while using
their interpretable construction to achieve a comparably low learned parameter
count. In this work, we propose an unrolled convolutional dictionary learning
network (CDLNet) and demonstrate its competitive denoising performance in both
low and high parameter count regimes. Specifically, we show that the proposed
model outperforms the state-of-the-art denoising models when scaled to similar
parameter count. In addition, we leverage the model's interpretable
construction to propose an augmentation of the network's thresholds that
enables state-of-the-art blind denoising performance and near-perfect
generalization on noise-levels unseen during training.
