基于主动判别机制的自适应生成对抗网络图像去模糊算法  

Adaptive image deblurring generative adversarial network algorithm based on active discrimination mechanism

作　　者：刘安阳 赵怀慈[2] 蔡文龙 许泽超 解瑞灯 LIU Anyang;ZHAO Huaici;CAI Wenlong;XU Zechao;XIE Ruideng(School of Electronics and Control Engineering,North China Institute of Aerospace Engineering,Langfang Hebei 065000,China;Shenyang Institute of Automation,Chinese Academy of Sciences,Shenyang Liaoning 110169,China)

机构地区：[1]北华航天工业学院电子与控制工程学院,河北廊坊065000 [2]中国科学院沈阳自动化研究所,沈阳110169

出　　处：《计算机应用》2023年第7期2288-2294,共7页journal of Computer Applications

基　　金：国家装备重大基础研究项目(51405-02A01);河北省重点研发计划项目(20327216D)。

摘　　要：针对现有图像去模糊算法在处理边缘丢失时出现弥散和伪影以及在视频处理中使用全帧去模糊方式导致不满足实时性需求的问题,提出一种基于主动判别机制的自适应生成对抗网络图像去模糊(ADBGAN)算法。首先,提出一种自适应模糊判别机制,开发了自适应模糊处理网络模块对输入图像进行模糊先验判断。在采集到输入时提前判断输入图像的模糊程度,从而剔除足够清晰的输入帧以提升算法运行效率。然后,在精细特征提取过程中引入注意力机制中的激励环节,从而在特征提取的流程中进行权重归一化来提升网络对精细特征的恢复能力。最后,在生成器架构中改进了特征金字塔精细特征恢复结构,并采用更轻量化的特征融合流程提高运行效率。为验证算法的有效性,在开源数据集GoPro和Kohler上进行了详细的对比实验。实验结果显示,在GoPro数据集中ADBGAN的视觉保真度是尺度循环网络(SRN)算法的2.1倍,并在峰值信噪比(PSNR)上较SRN算法提升了0.762 dB,具有良好的图像信息恢复能力;在视频数据处理时间上ADBGAN大幅超越了测试的所有算法,实测处理时间较SRN减少了85.9%。ADBGAN能够高效生成信息质量更高的去模糊图像。Aiming at the problems that existing image deblurring algorithms suffer from diffusion and artifacts when dealing with edge loss and the use of full-frame deblurring in video processing does not meet real-time requirements,an Adaptive DeBlurring Generative Adversarial Network(ADBGAN)algorithm based on active discrimination mechanism was proposed.Firstly,an adaptive fuzzy discrimination mechanism was proposed,and an adaptive fuzzy processing network module was developed to make a priori judgment of fuzziness on the input image.When collecting the input,the blurring degree of the input image was judged in advance,and the input frame which was clear enough was eliminated to improve the running efficiency of the algorithm.Then,the incentive link of the attention mechanism was introduced in the process of fine feature extraction,so that weight normalization was carried out in the forward flow of feature extraction to improve the performance of the network to recover fine-grained features.Finally,the feature pyramid fine feature recovery structure was improved in the generator architecture,and a more lightweight feature fusion process was adopted to improve the running efficiency.In order to verify the effectiveness of the algorithm,detailed comparison experiments were conducted on the open source datasets GoPro and Kohler.Experimental results on GoPro dataset show that the visual fidelity of ADBGAN is 2.1 times that of Scale-Recurrent Network(SRN)algorithm,the Peak Signal-to-Noise Ratio(PSNR)of ADBGAN is improved by 0.762 dB compared with that of SRN algorithm,and ADBGAN has good image information recovery ability;in terms of video processing time,the actual processing time is reduced by 85.9% compared to SRN.The proposed algorithm can generate deblurred images with higher information quality efficiently.

关 键 词：图像去模糊 图像去噪 生成对抗网络 自适应判别 特征恢复 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]