Y-net: Multi-scale feature aggregation network with wavelet structure similarity loss function for single image dehazing
Abstract
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.
Cite
@article{arxiv.2003.13912,
title = {Y-net: Multi-scale feature aggregation network with wavelet structure similarity loss function for single image dehazing},
author = {Hao-Hsiang Yang and Chao-Han Huck Yang and Yi-Chang James Tsai},
journal= {arXiv preprint arXiv:2003.13912},
year = {2020}
}
Comments
Accepted to IEEE ICASSP 2020