与传统的超分辨算法相比,基于卷积神经网络的超分辨算法具有较大优势,但仍存在训练时间较长、重建图像纹理不够清晰等问题。基于此,在原有的卷积神经网络超分辨重建算法基础上进行了以下优化:放弃原有的修正线性单元函数,改用新的激活函数;改变网络结构,图像重建由最后的反卷积上采样来实现;采用自适应矩估计优化算法替换原本的随机梯度下降优化算法。分别在Set5和Set14测试集上进行对比实验,实验结果表明,改进算法在较少的训练时间下,峰值信噪比最大提高了2.33 dB,纹理更加清晰,边缘更加完整,重建效果更好。

The super-resolution algorithm based on the convolutional neural network has great advantages compared with the traditional super-resolution algorithms. But there are still some problems to be improved, such as long training time, lacking of image texture reconstruction and so on. Owing to this, on the basis of the original convolutional neural network super-resolution reconstruction algorithm, the following optimizations are carried out. The original rectified linear unit function is discarded and the new activation function is used instead. The network structure is changed and image reconstruction is achieved by the final deconvolution upsampling. The original stochastic gradient descent optimization algorithm is replaced by adaptive moment estimation algorithm whose optimizes performance is faster and better. Comparative experiments are carried out on Set5 and Set14 test sets, respectively. The experimental results show that the reconstruction effects of the improved method with less training time are greatly improved on the objective evaluation index, for example, the power signal-to-noise ratio increases up to 2.33 dB, and the texture is clearer, the edges are more complete and the reconstruction effect is better on the subjective visual effects.