Y-Net: Multi-Scale Feature Aggregation Network With Wavelet Structure Similarity Loss Function For Single Image Dehazing

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 im...

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Bibliographic Details
Main Authors: Yang, Hao-Hsiang, Yang, Chao-Han Huck, James Tsai, Yi-Chang
Format: Conference Proceeding
Language:English
Subjects:
discrete wavelet transform
Image reconstruction
multi-scale feature aggregation
Network architecture
Periodic structures
Signal processing algorithms
Signal reconstruction
Single image dehazing
Speech processing
structure similarity
Training
Y-net
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Summary: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.
ISSN:2379-190X
DOI:10.1109/ICASSP40776.2020.9053920