An Improved U-Net Architecture for Image Dehazing
Published: 1 December 2021
IEICE Transactions on Information and Systems , pp 2218-2225; https://doi.org/10.1587/transinf.2021edp7043
Abstract: In this paper, we present a simple yet powerful deep neural network for natural image dehazing. The proposed method is designed based on U-Net architecture and we made some design changes to make it better. We first use Group Normalization to replace Batch Normalization to solve the problem of insufficient batch size due to hardware limitations. Second, we introduce FReLU activation into the U-Net block, which can achieve capturing complicated visual layouts with regular convolutions. Experimental results on public benchmarks demonstrate the effectiveness of the modified components. On the SOTS Indoor and Outdoor datasets, it obtains PSNR of 32.23 and 31.64 respectively, which are comparable performances with state-of-the-art methods. The code is publicly available online soon.
Keywords: neural / Image Dehazing / Net architecture / replace Batch Normalization / method / visual layouts
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