  Single image dehazing is the ill-posed two-dimensional signal reconstruction
problem. Recently, deep convolutional neural networks (CNN) have been
successfully used in many computer vision problems. In this paper, we propose a
Y-net that is named for its structure. This network reconstructs clear images
by aggregating multi-scale features maps. Additionally, we propose a Wavelet
Structure SIMilarity (W-SSIM) loss function in the training step. In the
proposed loss function, discrete wavelet transforms are applied repeatedly to
divide the image into differently sized patches with different frequencies and
scales. The proposed loss function is the accumulation of SSIM loss of various
patches with respective ratios. Extensive experimental results demonstrate that
the proposed Y-net with the W-SSIM loss function restores high-quality clear
images and outperforms state-of-the-art algorithms. Code and models are
available at https://github.com/dectrfov/Y-net.
