The use of deep convolutional neural networks (CNNs) for single image super-resolution (SISR) in the recent years has led to numerous vision-based applications. Complementing the growing interest in the computer vision community embracing such networks, there is an unmet demand of reduced computational complexity. Despite being state-of-the-art for SISR tasks, CNN-based models need to be compact and efficient to account for applications running on low-cost deployment devices that have limited computation resources. While it is common to note that many state-of-the-art SISR approaches stack large number of convolutional layers in order to enhance their SR performance, there is proportional increase in the computational complexity. We propose a computationally efficient, compact and enhanced progressive network for SISR task which we hereafter refer as ComPrESRNet. The architectural changes employs a progressive learning strategy with a novel design of Enhanced densely connected parallel residual network (EDPRN) which simultaneously extracts rich features from the low-resolution (LR) observation while reducing the total number of parameters to 2.61M making it compact in nature that is suitable for low computational platform. The novelty of the proposed model stems from the inclusion of (i) densely connected ResBlock to extract rich features in the LR observation, (ii) extended global residual learning approach which stabilizes the training process effectively and also helps network to further improve the SR performance and (iii) progressive upscaling module which can generate an SR image of size \(\times 4\) and \(\times 8\) of original LR image. The robustness of the proposed method is further demonstrated on four different benchmark testing datasets consisting of natural scenes and urban landscape to exemplify the different applications. The superior performance over other state-of-the-art methods is also illustrated in this work for an upscaling factor \(\times 4\) and \(\times 8\) despite the lower computational complexity. The code of the paper is available at https://github.com/Vishal2188/Compactand-Progressive-Networkfor-Enhanced-SISR---ComPrESRNet.

近年来，将深度卷积神经网络 (CNN) 用于单图像超分辨率 (SISR) 已导致许多基于视觉的应用。随着计算机视觉社区对此类网络的兴趣日益浓厚，降低计算复杂性的需求尚未得到满足。尽管是 SISR 任务的最新技术，但基于 CNN 的模型需要紧凑且高效，以解决在计算资源有限的低成本部署设备上运行的应用程序。虽然通常会注意到许多最先进的 SISR 方法堆叠大量卷积层以提高其 SR 性能，但计算复杂度成比例增加。我们提出了一个计算效率高的，ComPrESRNet。架构变化采用渐进式学习策略，采用增强型密集连接并行残差网络 (EDPRN) 的新颖设计，同时从低分辨率 (LR) 观察中提取丰富的特征，同时将参数总数减少到 2.61M，使其紧凑适用于低计算平台的性质。所提出模型的新颖性源于包含 (i) 密集连接的 ResBlock 以在 LR 观察中提取丰富的特征，(ii) 扩展的全局残差学习方法，可有效稳定训练过程并帮助网络进一步提高 SR 性能(iii) 渐进式放大模块，可以生成大小为\(\times 4\)和\(\times 8\)的 SR 图像原始 LR 图像。在由自然场景和城市景观组成的四个不同基准测试数据集上进一步证明了所提出方法的稳健性，以举例说明不同的应用。尽管计算复杂度较低，但这项工作也说明了对放大因子\(\times 4\)和\(\times 8\)的优越性能。论文代码可在 https://github.com/Vishal2188/Compactand-Progressive-Networkfor-Enhanced-SISR---ComPrESRNet 获得。