Deep convolutional neural networks can use hierarchical information to progressively extract structural information to recover high-quality images. However, preserving the effectiveness of the obtained structural information is important in image super-resolution. In this paper, we propose a cosine network for image super-resolution (CSRNet) by improving a network architecture and optimizing the training strategy. To extract complementary homologous structural information, odd and even heterogeneous blocks are designed to enlarge the architectural differences and improve the performance of image super-resolution. Combining linear and non-linear structural information can overcome the drawback of homologous information and enhance the robustness of the obtained structural information in image super-resolution. Taking into account the local minimum of gradient descent, a cosine annealing mechanism is used to optimize the training procedure by performing warm restarts and adjusting the learning rate. Experimental results illustrate that the proposed CSRNet is competitive with state-of-the-art methods in image super-resolution.

翻译：深度卷积神经网络能够利用层次化信息逐步提取结构信息以恢复高质量图像。然而，在图像超分辨率任务中，保持所获结构信息的有效性至关重要。本文通过改进网络架构和优化训练策略，提出了一种用于图像超分辨率的余弦网络（CSRNet）。为提取互补的同源结构信息，我们设计了奇偶异构模块以扩大架构差异并提升图像超分辨率性能。结合线性与非线性结构信息能够克服同源信息的缺陷，并增强图像超分辨率中所获结构信息的鲁棒性。考虑到梯度下降的局部极小值问题，采用余弦退火机制通过执行热重启与调整学习率来优化训练过程。实验结果表明，所提出的CSRNet在图像超分辨率任务中与现有先进方法相比具有竞争力。