基于权重量化与信息压缩的车载图像超分辨率重建  

Vehicle-based image super-resolution reconstruction based on weight quantification and information compression

作　　者：许德智 孙季丰[1] 罗莎莎 XU Dezhi;SUN Jifeng;LUO Shasha(School of Electronic and Information Engineering,South China University of Technology,Guangzhou Guangdong 510641,China)

机构地区：[1]华南理工大学电子与信息学院

出　　处：《计算机应用》2019年第12期3644-3649,共6页journal of Computer Applications

基　　金：广东省科技计划项目(x2dxB216005)~~

摘　　要：针对智能驾驶领域中需要在内存受限的情况下得到高质量的超分辨率图像的问题,提出一种基于权重八位二进制量化的车载图像超分辨率重建算法。首先,基于八位二进制量化卷积设计信息压缩模块,减少内部冗余,增强网络内信息流动,提高重建速率;然后,整个网络由一个特征提取模块、多个堆叠的信息压缩模块和一个图像重建模块构成,并利用插值后超分辨率空间的信息与低分辨率空间重建后的图像融合,在不增加模型复杂度的基础上,提高网络表达能力;最后,算法中整个网络结构基于对抗生成网络(GAN)框架进行训练,使得到的图片有更好主观视觉效果。实验结果表明,所提算法的车载图像重建结果的峰值信噪比(PSNR)比基于GAN的超分辨率重建(SRGAN)算法提高了0.22 dB,同时其生成模型大小缩小为LapSRN的39%,重建速度提高为LapSRN的7.57倍。For the intelligent driving field, it is necessary to obtain high-quality super-resolution images under the condition of limited memory. Therefore, a vehicle-based image super-resolution reconstruction algorithm based on weighted eight-bit binary quantization was proposed. Firstly, the information compression module was designed based on the eight-bit binary quantization convolution, reducing the internal redundancy, enhancing the information flow in the network, and improving the reconstruction rate. Then, the whole network was composed of a feature extraction module, a plurality of stacked information compression modules and an image reconstruction module, and the information of the interpolated super-resolution space was fused with the image reconstructed by the low-resolution space, improving the network expression ability without increasing the complexity of the model. Finally, the entire network structure in the algorithm was trained based on the Generative Adversarial Network(GAN) framework, making the image have better subjective visual effect. The experimental results show that, the Peak Signal-to-Noise Ratio(PSNR) of the proposed algorithm for the reconstructed vehicle-based image is 0.22 dB higher than that of Super-Resolution using GAN(SRGAN), its generated model size is reduced to 39% of that of the Laplacian pyramid Networks for fast and accurate Super-Resolution(LapSRN), and the reconstruction speed is improved to 7.57 times of that of LapSRN.

关 键 词：超分辨率重建 车载图像 八位二进制权重量化 对抗生成网络 信息压缩模块 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]