基于混洗池层的超分辨率密集U网
Dense U-net for super-resolution with shuffle pooling layer
摘要：
随着计算机视觉领域深度学习技术的发展，近年来单图像超分辨率研究取得了很大进展。在这些方法中，在特征提取之前，使用单个滤波器（通常为最大池化）将高分辨率输入图像缩小到低分辨率空间。这意味着特征提取是在有偏滤波的特征空间中执行的。我们证明，这是次优的，并导致信息丢失。在这项工作中，我们提出了一种最先进的卷积神经网络方法，称为具有洗牌池的密集U网。为了实现这一点，一个修改的U-网与密集块，称为密集U-网，提出了SISR。在此基础上，设计了一种新的池化策略--洗牌池化策略，以取代稠密U网进行下规模操作。通过这样做，我们有效地用专门为每个特征图训练的更多有损下采样滤波器替换了SISR管道中的手工滤波器，同时还减少了整个SISR操作的信息丢失。此外，提出了一种结合均方误差（MSE）、结构相似性指数（SSIM）和平均梯度误差（MGE）的混合损失函数，以减少感知损失和高层信息损失。我们所提出的方法实现了优越的准确性比以前的国家的最先进的三个基准数据集：SET 14，BSD 300，ICDAR 2003。代码可在线获取。
英文摘要：
Recent researches have achieved great progress on single image super-resolution(SISR) due to the development of deep learning in the field of computer vision. In these method, the high resolution input image is down-scaled to low resolution space using a single filter, commonly max-pooling, before feature extraction. This means that the feature extraction is performed in biased filtered feature space. We demonstrate that this is sub-optimal and causes information loss. In this work, we proposed a state-of-the-art convolutional neural network method called Dense U-net with shuffle pooling. To achieve this, a modified U-net with dense blocks, called dense U-net, is proposed for SISR. Then, a new pooling strategy called shuffle pooling is designed, which is aimed to replace the dense U-Net for down-scale operation. By doing so, we effectively replace the handcrafted filter in the SISR pipeline with more lossy down-sampling filters specifically trained for each feature map, whilst also reducing the information loss of the overall SISR operation. In addition, a mix loss function, which combined with Mean Square Error(MSE), Structural Similarity Index(SSIM) and Mean Gradient Error (MGE), comes up to reduce the perception loss and high-level information loss. Our proposed method achieves superior accuracy over previous state-of-the-art on the three benchmark datasets: SET14, BSD300, ICDAR2003. Code is available online.
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