  Neural architecture search (NAS) has recently reshaped our understanding on
various vision tasks. Similar to the success of NAS in high-level vision tasks,
it is possible to find a memory and computationally efficient solution via NAS
with highly competent denoising performance. However, the optimization gap
between the super-network and the sub-architectures has remained an open issue
in both low-level and high-level vision. In this paper, we present a novel
approach to filling in this gap by connecting model-guided design with NAS
(MoD-NAS) and demonstrate its application into image denoising. Specifically,
we propose to construct a new search space under model-guided framework and
develop more stable and efficient differential search strategies. MoD-NAS
employs a highly reusable width search strategy and a densely connected search
block to automatically select the operations of each layer as well as network
width and depth via gradient descent. During the search process, the proposed
MoG-NAS is capable of avoiding mode collapse due to the smoother search space
designed under the model-guided framework. Experimental results on several
popular datasets show that our MoD-NAS has achieved even better PSNR
performance than current state-of-the-art methods with fewer parameters, lower
number of flops, and less amount of testing time.
