论文标题
用于指纹图像DeNoising和Inpainting的深层编码器神经网络
Deep Encoder-Decoder Neural Network for Fingerprint Image Denoising and Inpainting
论文作者
论文摘要
指纹图像Deoising是指纹识别的非常重要的一步。为了改善指纹图像的脱氧作用，我们设计了一种基于深层编码器decoder网络的指纹定位算法，该网络编码子网编码噪声图像的指纹特征。解码器子网在不提高膨胀的情况下，以增加延伸的特征，同时又增加了质量的统治，同时又可以提高该网络，从而提高了该网络，以提高统一的统治，以达到延伸的统治，使同量统一的同量统一性，使其在膨胀方面提高统治，使同量统一均匀衡量。推理速度。此外，通过引入残差学习，可以在不同级别的网络上进行特征融合，从而进一步恢复指纹的详细特征并改善脱氧作用。最后，实验结果表明，与其他方法相比，该算法可以更好地恢复指纹图像中的边缘，线和曲线特征，具有更好的视觉效果和更高的峰值信噪比（PSNR）。
Fingerprint image denoising is a very important step in fingerprint identification. to improve the denoising effect of fingerprint image,we have designs a fingerprint denoising algorithm based on deep encoder-decoder network,which encoder subnet to learn the fingerprint features of noisy images.the decoder subnet reconstructs the original fingerprint image based on the features to achieve denoising, while using the dilated convolution in the network to increase the receptor field without increasing the complexity and improve the network inference speed. In addition, feature fusion at different levels of the network is achieved through the introduction of residual learning, which further restores the detailed features of the fingerprint and improves the denoising effect. Finally, the experimental results show that the algorithm enables better recovery of edge, line and curve features in fingerprint images, with better visual effects and higher peak signal-to-noise ratio (PSNR) compared to other methods.
