论文标题
单图超分辨率的多级特征融合机制
Multi-Level Feature Fusion Mechanism for Single Image Super-Resolution
论文作者
论文摘要
卷积神经网络（CNN）已被广泛用于单像超级分辨率（SISR），因此SISR最近取得了巨大的成功。随着网络的加深，网络的学习能力变得越来越强大。但是，大多数基于CNN的SISR方法并未完全使用层次结构功能和网络的学习能力。这些特征不能直接通过后续层提取，因此上一层层次信息对随后层相对较差的输出和性能几乎没有影响。为了解决上述问题，提出了一种新型的多级特征融合网络（MLRN），可以充分利用全局中间功能。我们还将功能Skip Fusion Block（FSFBLOCK）作为基本模块介绍。每个块可以直接提取到原始的多尺度功能和融合多级功能，然后学习特征空间相关。整体方法的特征之间的相关性导致信息机制的连续全局记忆。公共数据集上的广泛实验表明，可以实施MLRN提出的方法，这对于最先进的方法是有利的。
Convolution neural network (CNN) has been widely used in Single Image Super Resolution (SISR) so that SISR has been a great success recently. As the network deepens, the learning ability of network becomes more and more powerful. However, most SISR methods based on CNN do not make full use of hierarchical feature and the learning ability of network. These features cannot be extracted directly by subsequent layers, so the previous layer hierarchical information has little impact on the output and performance of subsequent layers relatively poor. To solve above problem, a novel Multi-Level Feature Fusion network (MLRN) is proposed, which can take full use of global intermediate features. We also introduce Feature Skip Fusion Block (FSFblock) as basic module. Each block can be extracted directly to the raw multiscale feature and fusion multi-level feature, then learn feature spatial correlation. The correlation among the features of the holistic approach leads to a continuous global memory of information mechanism. Extensive experiments on public datasets show that the method proposed by MLRN can be implemented, which is favorable performance for the most advanced methods.
