盲去模糊的多尺度编解码深度卷积网络

针对拍摄场景中物体运动不一致所带来的非均匀模糊,为提高复杂运动场景中去模糊的效果,提出一种多尺度编解码深度卷积网络。该网络采用"从粗到细"的多尺度级联结构,在模糊核未知条件下,实现盲去模糊;其中,在该网络的编解码模块中,提出一种快速多尺度残差块,使用两个感受野不同的分支增强网络对多尺度特征的适应能力;此外,在编解码之间增加跳跃连接,丰富解码端信息。与2018年国际计算机视觉与模式识别会议（CVPR）上提出的多尺度循环网络相比,峰值信噪比（PSNR）高出0.06 dB;与2017年CVPR上提出的深度多尺度卷积网络相比,峰值信噪比和平均结构相似性（MSSIM）分别提高了1.4%和3.2%。实验结果表明,该网络能快速去除图像模糊,恢复出图像原有的边缘结构和纹理细节。

Deep multi-scale encoder-decoder convolutional network for blind deblurring

Aiming at the heterogeneous blur of images caused by inconsistent motion of objects in the shooting scene, a deep multi-scale encoder-decoder convolutional network was proposed to improve the deblurring effect in complex motion scenes. A multi-scale cascade structure named "from coarse to fine" was applied to this network, and blind deblurring was achieved with the blur kernel unknown. In the encoder-decoder module of the network, a fast multi-scale residual block was proposed, which used two branches with different receptive fields to enhance the adaptability of the network to multi-scale features. In addition, skip connections were added between the encoder and the decoder to enrich the information of the decoder. The Peak Signal-to-Noise Ratio (PSNR) value pf this network is 0.06 dB higher than that of the Scale-recurrent Network proposed on CVPR(Conference on Computer Vision and Pattern Recognition)2018; the PSNR and Mean Structural Similarity (MSSIM) values are increased by 1.4% and 3.2% respectively compared to those of the deep multi-scale convolution network proposed on CVPR2017. The experimental results show that the proposed network can deblur the image quickly and restore the edge structure and texture details of the image.