Neural Architecture Search for Image Dehazing

Abstract

Manual design of deep networks require numerous trials and parameter tuning, resulting in inefficient utilization of time, energy, and resources. In this work, 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 the gradient based search strategy and hierarchical networklevel 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 AutoDehazeB, AutoDehazeU1, AutoDehazeU2, and AutoDehazeL which are inspired by the boat-shaped, Ushaped, and lateral connection-based designs. To the best of our knowledge, this is a first attempt to present a NAS method for dehazing with a variety of network search strategies. We conduct a comprehensive set of experiments on Reside-Standard (SOTS), Reside-β (SOTS) and Reside-β (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.