  Recently, neural architecture search (NAS) methods have attracted much
attention and outperformed manually designed architectures on a few high-level
vision tasks. In this paper, we propose HiNAS (Hierarchical NAS), an effort
towards employing NAS to automatically design effective neural network
architectures for image denoising. HiNAS adopts gradient based search
strategies and employs operations with adaptive receptive field to build an
flexible hierarchical search space. During the search stage, HiNAS shares cells
across different feature levels to save memory and employ an early stopping
strategy to avoid the collapse issue in NAS, and considerably accelerate the
search speed. The proposed HiNAS is both memory and computation efficient,
which takes only about 4.5 hours for searching using a single GPU. We evaluate
the effectiveness of our proposed HiNAS on two different datasets, namely an
additive white Gaussian noise dataset BSD500, and a realistic noise dataset
SIM1800. Experimental results show that the architecture found by HiNAS has
fewer parameters and enjoys a faster inference speed, while achieving highly
competitive performance compared with state-of-the-art methods. We also present
analysis on the architectures found by NAS. HiNAS also shows good performance
on experiments for image de-raining.
