基于双重控制网络的图像去模糊方法

传统的图像复原问题主要采用分治法，即将图像复原问题分为不同的子问题，通过对子问题处理得到最优解。由于不同处理环节之间的衔接和损失问题，子问题的最优解并不能得到全局最优解。针对此问题，提出一种端到端的双重控制网络，采用控制模块通过参数分别控制退化分支和处理分支。该网络通过特殊的编解码器结构处理特征问题——在固定尺度因子子网下，采用循环跳跃连接结构消除卷积层的堆叠块，增强输出端的特征显示。实验证明，提出方法与对比方法复原的图像峰值信噪比(PSNR)数值在30以上，结构相似度(SSIM)指标在0.90以上，且有效的改善了视觉效果。

Dual control network for image deblurring

The traditional image restoration problem mainly adopts the divide-and-conquer method, image restoration problem is divided into different sub-problems, and the optimal solution is obtained by processing the sub-problems. Due to the connection and loss between different processing links, the optimal solution of the sub-problem cannot be the global optimal solution. To solve this problem, an end-to-end dual control network is proposed, which uses a control module to control the degenerate branch and the processing branch through parameters. The network uses a special Encode-Decode structure to deal with the feature problem under the fixed scale factor subnet, the loop skip connection structure is used to eliminate the stacking blocks of the convolutional layer and enhance the feature display at the output end. Experiments show that the peak signal-to-noise ratio (PSNR) value of the image restored by the proposed method and the comparison method is above 30, and the structural similarity index measure (SSIM) is above 0.90, which effectively improves the visual effect.