论文标题
使用分割至上的自我发明生成对抗网络的超级分辨率
Super Resolution Using Segmentation-Prior Self-Attention Generative Adversarial Network
论文作者
论文摘要
卷积神经网络（CNN）由于其出色的性能而进行了强烈实施，以解决超级分辨率（SR）任务。但是，由于缺乏先验知识和CNN的小型接受领域，超级分辨率的问题仍然具有挑战性。我们提出了分割 - 突发性的自我发作生成对抗网络（SPSAGAN），以将分割 - 基准和特征注意力结合到一个统一的框架中。这种组合是由精心设计的加权添加来领导的，以平衡特征和细分注意力的影响，因此网络可以强调同一分割类别中的纹理，同时侧重于长距离特征关系。我们还提出了一个轻巧的跳过连接体系结构，称为残留稀疏块（RRSB），以进一步改善超分辨率性能并节省计算。广泛的实验表明，与许多SR数据集中的最先进的Sftgan和Esrgan相比，SPSAGAN可以产生更现实和令人愉悦的纹理。
Convolutional Neural Network (CNN) is intensively implemented to solve super resolution (SR) tasks because of its superior performance. However, the problem of super resolution is still challenging due to the lack of prior knowledge and small receptive field of CNN. We propose the Segmentation-Piror Self-Attention Generative Adversarial Network (SPSAGAN) to combine segmentation-priors and feature attentions into a unified framework. This combination is led by a carefully designed weighted addition to balance the influence of feature and segmentation attentions, so that the network can emphasize textures in the same segmentation category and meanwhile focus on the long-distance feature relationship. We also propose a lightweight skip connection architecture called Residual-in-Residual Sparse Block (RRSB) to further improve the super-resolution performance and save computation. Extensive experiments show that SPSAGAN can generate more realistic and visually pleasing textures compared to state-of-the-art SFTGAN and ESRGAN on many SR datasets.
