论文标题
Y-NET：具有小波结构相似性损失函数的多尺度特征聚合网络的单图像
Y-net: Multi-scale feature aggregation network with wavelet structure similarity loss function for single image dehazing
论文作者
论文摘要
单图像脱掩的是二维信号重建问题。最近，深层卷积神经网络（CNN）已成功用于许多计算机视觉问题。在本文中，我们提出了一个以其结构命名的Y网络。该网络通过汇总多尺度特征地图来重建清晰的图像。此外，我们在训练步骤中提出了小波结构相似性（W-SSIM）损失函数。在提出的损耗函数中，将离散小波变换反复应用，以将图像分为不同频率和尺度的不同尺寸的斑块。提出的损失函数是各种比率的SSIM损失的积累。广泛的实验结果表明，具有W-SSIM损耗函数的Y-NET恢复了高质量的清晰图像，并且优于最先进的算法。代码和型号可在https://github.com/dectrfov/y-net上找到。
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.
