通过异构块与余弦退火，提升图像超分辨率的结构保真度。
A Cosine Network for Image Super-Resolution
- 设计奇偶异构块，提取互补结构信息增强网络表达能力。
- 融合线性与非线性结构信息，提升结果鲁棒性。
- 采用余弦退火优化训练，避免梯度陷入局部最优。
深度卷积神经网络可逐层提取结构信息以恢复高质量图像。然而，保持所获结构信息的有效性在图像超分辨率中至关重要。本文提出一种用于图像超分辨率的余弦网络（CSRNet），通过改进网络架构并优化训练策略实现性能提升。为提取互补的同源结构信息，设计奇偶异构块以扩大架构差异，从而提高图像超分辨率性能。结合线性与非线性结构信息可克服同源信息的局限性，增强所得结构信息的鲁棒性。针对梯度下降易陷入局部最小的问题，采用余弦退火机制进行训练优化，通过预热重启和学习率调整提升收敛效果。实验表明，所提出的CSRNet在图像超分辨率任务中具有与当前先进方法相当的竞争力。
原文摘要 · Abstract (English)
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.
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