论文标题
感知性极端超级分辨率网络，带有接收场块
Perceptual Extreme Super Resolution Network with Receptive Field Block
论文作者
论文摘要
对于单图像，感知性极端分辨率非常困难，因为不同图像的纹理细节差异很大。为了解决这个困难，我们开发了一个基于增强的SRGAN的接受场块的超级分辨率网络。我们称我们的网络RFB-ESRGAN。主要贡献列出如下。首先，为了提取多尺度信息并增强特征可区分性，我们将接受场块（RFB）应用于超级分辨率。 RFB在对象检测和分类方面取得了竞争成果。其次，RFB中使用了几个小核，而不是在多尺度接收场块中使用大卷积内核，这使我们能够提取详细的功能并降低计算复杂性。第三，我们或在UPS采样阶段交替使用不同的UP抽样方法来降低高计算的复杂性，并且仍然保持令人满意的性能。第四，我们使用10个不同迭代模型的合奏来改善模型的鲁棒性，并减少每个单独模型引入的噪声。我们的实验结果表明RFB-ESRGAN的出色表现。根据NTIRE 2020感知极端超分辨率挑战的初步结果，我们的解决方案在所有参与者中排名第一。
Perceptual Extreme Super-Resolution for single image is extremely difficult, because the texture details of different images vary greatly. To tackle this difficulty, we develop a super resolution network with receptive field block based on Enhanced SRGAN. We call our network RFB-ESRGAN. The key contributions are listed as follows. First, for the purpose of extracting multi-scale information and enhance the feature discriminability, we applied receptive field block (RFB) to super resolution. RFB has achieved competitive results in object detection and classification. Second, instead of using large convolution kernels in multi-scale receptive field block, several small kernels are used in RFB, which makes us be able to extract detailed features and reduce the computation complexity. Third, we alternately use different upsampling methods in the upsampling stage to reduce the high computation complexity and still remain satisfactory performance. Fourth, we use the ensemble of 10 models of different iteration to improve the robustness of model and reduce the noise introduced by each individual model. Our experimental results show the superior performance of RFB-ESRGAN. According to the preliminary results of NTIRE 2020 Perceptual Extreme Super-Resolution Challenge, our solution ranks first among all the participants.
