In recent years, the convolutional neural networks (CNNs) have been successfully applied to single image super-resolution (SISR) task. However, most of the CNN based SISR methods obtain better performance with a huge amount of training parameters which increases the computational complexity of their SISR models. Such SR networks suffer from a heavy burden on computational assets and as a result, they are no longer appropriate for many real-world applications. Hence, in the computer vision community, it is an interest to endorse an SR approach which makes use of less number of training parameters with better SR performance. In this paper, we propose a computationally efficient SR approach called enhanced progressive super-resolution network i.e., E-ProSRNet. This approach is the enhanced version of our base proposed model called ProSRNet. In E-ProSRNet model, we propose a novel enhanced parallel densely connected residual network (E-PDRN) which helps to extract rich features in the low-resolution (LR) observation. The SR performance of proposed E-ProSRNet model is better than that of ProSRNet and it uses a less number of training parameters when compared to that of ProSRNet model. The experimental analysis on common testing benchmark datasets shows that the proposed E-ProSRNet sets new state-of-the-art performance on SISR task for upscaling factor $\times 4$. The E-ProSRNet method obtains better SR performance when compared to that obtained using proposed ProSRNet as well as the other state-of-the-art methods with significant reduction in the computational complexity.

近年来，卷积神经网络（CNN）已成功应用于单图像超分辨率（SISR）任务。但是，大多数基于CNN的SISR方法通过大量的训练参数可获得更好的性能，从而增加了其SISR模型的计算复杂性。这种SR网络给计算资产带来沉重负担，因此，它们不再适合于许多实际应用。因此，在计算机视觉界，认可一种SR方法是有意义的，该方法使用较少数量的训练参数并具有更好的SR性能。在本文中，我们提出了一种计算有效的SR方法，称为增强型渐进超分辨率网络，即E-ProSRNet。这种方法是我们提出的基本模型ProSRNet的增强版本。在E-ProSRNet模型中，我们提出了一种新颖的增强型并行紧密连接残差网络（E-PDRN），该网络有助于提取低分辨率（LR）观测中的丰富特征。所提出的E-ProSRNet模型的SR性能优于ProSRNet，并且与ProSRNet模型相比，它使用较少的训练参数。对通用测试基准数据集的实验分析表明，所提出的E-ProSRNet为SISR任务设置了新的最新性能，以提高比例因子$\times 4$。与使用建议的ProSRNet和其他最新方法相比，E-ProSRNet方法可获得更好的SR性能，并且大大降低了计算复杂性。