Haris Muhammad, Widyanto M Rahmat, Nobuhara Hajime
Appl Opt. 2017 Aug 1;56(22):6043-6048. doi: 10.1364/AO.56.006043.
An efficient network for super-resolution, which we refer to as inception learning super-resolution (ILSR), is proposed. We adopt the inception module from GoogLeNet to exploit multiple features from low-resolution images, yet maintain fast training steps. The proposed ILSR network demonstrates low computation time and fast convergence during the training process. It is divided into three parts: feature extraction, mapping, and reconstruction. In feature extraction, we apply the inception module followed by dimensional reduction. Then, we map features using a simple convolutional layer. Finally, we reconstruct the high-resolution component using the inception module and a 1×1 convolutional layer. Experimental results demonstrate that the proposed network can construct sharp edges and clean textures, and reduce computation time by up to three orders of magnitude compared to state-of-the-art methods.
我们提出了一种高效的超分辨率网络，称为inception学习超分辨率（ILSR）。我们采用来自GoogLeNet的inception模块来利用低分辨率图像的多个特征，同时保持快速的训练步骤。所提出的ILSR网络在训练过程中展示出低计算时间和快速收敛性。它分为三个部分：特征提取、映射和重建。在特征提取中，我们应用inception模块，然后进行降维。接着，我们使用一个简单的卷积层来映射特征。最后，我们使用inception模块和一个1×1卷积层来重建高分辨率分量。实验结果表明，与现有方法相比，所提出的网络能够构建清晰的边缘和干净的纹理，并将计算时间减少多达三个数量级。
