Laser & Optoelectronics Progress, Volume. 54, Issue 11, 111005(2017)
Super-Resolution Reconstruction of Image Based on Optimized Convolution Neural Network
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.
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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
Category: Image Processing
Received: May. 25, 2017
Accepted: --
Published Online: Nov. 17, 2017
The Author Email: Kexin Liu (m18602907837@163.com)