论文标题
浓密的U-NET，用于带混洗的超分辨率
Dense U-net for super-resolution with shuffle pooling layer
论文作者
论文摘要
由于计算机视觉领域的深度学习发展，最近的研究在单图像超分辨率（SISR）方面取得了巨大进展。在这些方法中，高分辨率输入图像在特征提取之前使用单个过滤器（通常是最大填充）将缩小为低分辨率空间。这意味着特征提取是在偏置过滤的特征空间中执行的。我们证明这是亚最佳的，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“密集的U-net”，带有洗牌集合。为此，为SISR提出了一个具有致密块（称为密度U-net）的修改的U-NET。然后，设计了一种称为洗牌池的新池策略，旨在替换密集的U-NET以进行下限操作。通过这样做，我们有效地替换了SISR管道中的手工制作的过滤器，并为每个功能映射专门训练了更多有损的下采样过滤器，同时还减少了整体SISR操作的信息损失。此外，混合损失函数与均方根误差（MSE），结构相似性指数（SSIM）和平均梯度误差（MGE）相结合，以减少感知损失和高级信息损失。我们提出的方法在三个基准数据集上的先前最先前的方法具有优越的精度：SET14，BSD300，ICDAR2003。代码可在线提供。
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.
