Abstract:
Abstract:
Manual design of deep networks require numerous trials and parameter tuning, resulting in inefficient utilization of time, energy, and resources. In this article, we present a neural architecture search (NAS) algorithm—AutoDehaze, to automatically discover effective neural network for single image dehazing. The proposed AutoDehaze algorithm is built on the gradient-based search strategy and hierarchical network-level optimization. We construct a set of search space layouts to reduce memory consumption, avoid the NAS collapse issue, and considerably accelerate the search speed. We propose four search spaces $\text{AutoDehaze}_{B}$ , $\text{AutoDehaze}_{U1}$ , $\text{AutoDehaze}_{U2}$ , and $\text{AutoDehaze}_{L}$ , which are inspired by the boat-shaped, U-shaped, and lateral connection-based designs. To the best of authors knowledge, this is a first attempt to present an NAS method for dehazing with a variety of network search strategies. We conduct a comprehensive set of experiments on Reside-Standard (SOTS), Reside- $\beta$ (SOTS) and Reside- $\beta$ (HSTS), D-Hazy, and HazeRD datasets. The architectures discovered by the proposed AutoDehaze quantitatively and qualitatively outperform the existing state-of-the-art approaches. The experiments also show that our models have considerably fewer parameters and runs at a faster inference speed in both CPU and GPU devices.
