论文标题
图像超分辨率具有增强的组卷积神经网络
Image Super-resolution with An Enhanced Group Convolutional Neural Network
论文作者
论文摘要
具有强大学习能力的CNN被广泛选择以解决超分辨率问题。但是，CNN依靠更深的网络体系结构来提高图像超分辨率的性能，这通常会增加计算成本。在本文中，我们通过完全融合了深层和宽的通道特征，提出了一个增强的超分辨率组CNN（ESRGCNN），它通过完全融合了深层和宽的通道特征，以单图超级分辨率（SISR）中不同通道的相关性提取更准确的低频信息。同样，ESRGCNN中的信号增强操作对于继承更长距离上下文信息以解决长期依赖性也很有用。自适应上采样操作被收集到CNN中，以获得具有不同大小的低分辨率图像的图像超分辨率模型。广泛的实验报告说，我们的ESRGCNN在SISR中的SISR性能，复杂性，执行速度，图像质量评估和SISR的视觉效果方面超过了最先进的实验。代码可在https://github.com/hellloxiaotian/esrgcnn上找到。
CNNs with strong learning abilities are widely chosen to resolve super-resolution problem. However, CNNs depend on deeper network architectures to improve performance of image super-resolution, which may increase computational cost in general. In this paper, we present an enhanced super-resolution group CNN (ESRGCNN) with a shallow architecture by fully fusing deep and wide channel features to extract more accurate low-frequency information in terms of correlations of different channels in single image super-resolution (SISR). Also, a signal enhancement operation in the ESRGCNN is useful to inherit more long-distance contextual information for resolving long-term dependency. An adaptive up-sampling operation is gathered into a CNN to obtain an image super-resolution model with low-resolution images of different sizes. Extensive experiments report that our ESRGCNN surpasses the state-of-the-arts in terms of SISR performance, complexity, execution speed, image quality evaluation and visual effect in SISR. Code is found at https://github.com/hellloxiaotian/ESRGCNN.
