激光与光电子学进展, 2017, 54 (11): 111005, 网络出版: 2017-11-17   

基于优化卷积神经网络的图像超分辨率重建 下载: 1498次

Super-Resolution Reconstruction of Image Based on Optimized Convolution Neural Network
作者单位
西安建筑科技大学信息与控制工程学院, 陕西 西安 710055
摘要
与以往两类单帧图像的超分辨率重建方法相比,卷积神经网络超分辨率(SRCNN)技术以其端对端的映射架构大幅提高了运行效率与复原精准度,然而网络的层数限制以及收敛性能使得部分图像的恢复效果不及基于样例的重建方法。针对网络优化问题,提出了一种将粒子群优化(PSO)算法与SRCNN相结合的方法,利用PSO算法对网络权重进行初始化,同时结合梯度下降(GD)算法对权值进行修正,使得PSO算法的全局搜索能力与GD算法的局部寻优能力相融合。分别对set5、set14数据集和雾霾天气下模糊图片进行对比实验,结果表明,所提算法不仅能以较少参数来获得较高性能的网络,其重建效果优于已有的4种算法,而且对边缘的锐化能力更强。
Abstract
Compared with the previous two types of single-frame image super-resolution reconstruction algorithm, the super-resolution with convolution neural network (SRCNN) has greatly improved the operational efficiency and recovery accuracy with its end-to-end mapping structure. However, the number of hidden layers and the convergence performance of the network make the recovery effects of some images worse than the example-based reconstruction algorithms. In view of the problem of network optimization, the algorithm of combining particle swarm optimization (PSO) with SRCNN is proposed. PSO is used to initialize the network weight and the gradient descent (GD) algorithm is used to correct the weight which can combine the global search capability of PSO and the local search ability of GD. The experimental results of set5, set14 datasets and the blurred images under haze weather respectively show that the proposed algorithm can not only use less parameters to obtain higher performance network, but also has better reconstruction effect than the existing four algorithms, and the ability to sharpen edges is stronger.

王民, 刘可心, 刘利, 杨润玲. 基于优化卷积神经网络的图像超分辨率重建[J]. 激光与光电子学进展, 2017, 54(11): 111005. Wang Min, Liu Kexin, Liu Li, Yang Runling. Super-Resolution Reconstruction of Image Based on Optimized Convolution Neural Network[J]. Laser & Optoelectronics Progress, 2017, 54(11): 111005.

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