基于生成对抗网络的行人异常行为图像去模糊算法研究

Deblurring algorithm based on pedestrian abnormal behavior generation countermeasure network

为解决在行为异常检测中遇到的运动模糊问题,提出一种基于DeblurGAN改进的快速去运动模糊算法。使用3个3×3的卷积替换原生成器中的7×7的卷积,并舍弃原算法上采样时使用的转置卷积,对需要上采样的特征图进行双线性插值。将原算法生成器结构中的残差单元替换成密集残差块(RRDB),然后将得到的残差特征缩放到0∼1之间的值,避免训练不稳定。在原生成器的损失函数中添加梯度图像的L1损失,增加图像的边缘信息使重建后的图像边缘更明显,克服了DeblurGAN重建图像边缘细节不够清晰的缺陷。经实验验证,并和文献[14]、文献[18]进行比较,结果显示:优化后的模型与DeblurGAN相比,峰值信噪比提高0.94,结构相似度和速度相当,并解决了重建后图像棋盘格子的问题,细节边缘更加突出,模型性能优于相关算法。

To solve the problem of motion blur in abnormal behavior detection,a fast motion blur removal algorithm,based on DeblurGAN,is proposed.Three 3×3 convolutions are used to replace the 7×7 convolution in the original generator.The transposed convolution is discarded.Firstly,bilinear interpolation is used to expand the size of the feature map which needs upsampling.The residual unit is replaced by a residual density block(RRDB)in the original algorithm.The RRDB is then scaled to 0∼1 to avoid unstable training.The L1 loss of gradient images is added to the loss function of the original generator.As the DeblurGAN reconstructed image edge is often not clear enough,the edge information of the image is added to make the reconstructed image edge more obvious.The effectiveness of this method is verified by experiments and is compared with other similar algorithms like DeblurGAN.The PSNR of the optimized model is improved by 0.94.The structure similarity and speed are equivalent.The chessboard lattice problem in the reconstructed image is solved.The edge of detail is more prominent.The performance of the pro-posed model is better than that of other related algorithms.