基于轻量自动残差缩放网络的图像超分辨率重建

近年来,由于深度卷积神经网络的出色性能,深度学习已成为图像超分辨率领域的研究热点,已经有许多具有很深结构的大型模型被提出。而在实际应用中,普通个人计算机或智能终端的硬件显然不适合大规模深度神经网络模型。提出了一种针对单幅图像超分辨率且具有自动残差缩放功能的轻量级网络(ARSN),与许多基于深度学习的方法相比,它的层和参数更少。此外,该网络中有特殊的残差块和跳跃连接用来进行残差缩放以及全局和局部残差学习。根据测试数据集结果,该网络在重建质量和运行速度上都达到了非常优异的性能。所提出的网络在性能、速度和硬件消耗方面均取得了良好的效果,具有较高的实用价值。

Light-weight automatic residual scaling network for image super-resolution reconstruction

Recently, deep learning has been a hot research topic in the field of image super-resolution due to the excellent performance of deep convolutional neural networks. Many large-scale models with very deep structures have been proposed. However, in practical applications, the hardware of ordinary personal computers or smart terminals are obviously not suitable for large-scale deep neural network models. A light-weight Network with Automatic Residual Scaling (ARSN) for single image super-resolution was proposed, which has fewer layers and parameters compared with many other deep learning based methods. In addition, the specified residual blocks and skip connections in this network were utilized for residual scaling, global and local residual learning. The results on test datasets show that this model achieves state-of-the-art performance on both reconstruction quality and running speed. The proposed network achieves good results in terms of performance, speed and hardware consumption， and has high practical value.