分离表示学习下的严重缺失静脉信息高质量生成

Disentangled representation learning network for high⁃quality vein image inpainting

为解决在识别过程中存在手背静脉图像信息严重缺失而造成识别效率低下的问题,提出基于分离表示学习严重缺失手背静脉图像的修复算法.基于图像到图像转换的互信息估计表示学习的原理,通过一个共享属性部分编码网络和一个独占属性部分的编码网络来进行特征信息的分离表示,学习静脉关键点与完整静脉骨架图像之间的映射,进而实现基于部分关键点对静脉严重缺失图像的良好修复.为保证生成图像的质量,采用对抗损失与感知损失保证图像的语义真实性与信息完整性,采用循环一致性损失对分离表示网络得到的分离内容和属性表示的循环重建进行约束.实验结果表明,生成图像在视觉效果、峰值信噪比(Peak Signal to Noise Ratio,PSNR)、结构相似性(Structural Similarity Index,SSIM)等方面的表现优于经典算法,有效地实现了对严重缺失静脉图像的良好修复.

To establish a stable and effective hand vein image identification system and solve the problem of low recognition efficiency caused by the serious lack of hand vein image information in the recognition process,it is necessary to inpainting the missing information of hand⁃dorsal vein image.Based on the disentangled representation learning of mutual information estimation in the image⁃to⁃image translation,we propose a method that uses a shared part coding network and an exclusive attribute part coding network to separate representation information of image features,and learn the mapping between the key points and the complete skeleton of vein image to achieve good inpainting of the seriously missing vein image based on some key points.To ensure the quality of the generated image,the adversarial loss and perceptual loss are used to ensure the semantic authenticity and information integrity of the image,and cycle consistency loss is used to constrain the separated content and attribute representation of the separated representation network.The experimental results show that the visual effect,Peak Signal to Noise Ratio(PSNR)and Structural Similarity Index(SSIM)of the generated image is better than the existing classical algorithms,which effectively tests the good inpainting result of the seriously missing vein image.