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首页> 外文期刊>International journal of digital crime and forensics >Optimization-Driven Kernel and Deep Convolutional Neural Network for Multi-View Face Video Super Resolution
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Optimization-Driven Kernel and Deep Convolutional Neural Network for Multi-View Face Video Super Resolution

机译：用于多视图脸超分辨率的优化驱动的内核和深卷积神经网络

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摘要

One of the major challenges faced by video surveillance is recognition from low-resolution videos or person identification. Image enhancement methods play a significant role in enhancing the resolution of the video. This article introduces a technique for face super resolution based on a deep convolutional neural network (Deep CNN). At first, the video frames are extracted from the input video and the face detection is performed using the Viola-Jones algorithm. The detected face image and the scaling factors are fed into the Fractional-Grey Wolf Optimizer (FGWO)-based kernel weighted regression model and the proposed Deep CNN separately. Finally, the results obtained from both the techniques are integrated using a fuzzy logic system, offering a face image with enhanced resolution. Experimentation is carried out using the UCSD face video dataset, and the effectiveness of the proposed Deep CNN is checked depending on the block size and the upscaling factor values and is evaluated to be the best when compared to other existing techniques with an improved SDME value of 80.888.
机译：视频监控面临的主要挑战之一是从低分辨率视频或人员身份识别的认可。图像增强方法在增强视频的分辨率方面发挥着重要作用。本文介绍了一种基于深度卷积神经网络（深CNN）面部超分辨率的技术。首先，从输入视频中提取视频帧，并且使用Viola-Jones算法执行面部检测。检测到的面部图像和缩放因子被送入分数灰狼优化器（FGWO）基础的内核加权回归模型，并分别提出的深层CNN。最后，通过模糊逻辑系统集成了从两种技术获得的结果，提供具有增强分辨率的面部图像。使用UCSD面部视频数据集进行实验，并且根据块大小和升高系数值检查所提出的深CNN的有效性，并且与其他现有技术相比，评估为最佳的具有改进的SDME值的基础80.888。

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