基于卷积稀疏自编码的图像超分辨率重建

Convolutional sparse auto-encoder for image super-resolution reconstruction

针对卷积稀疏编码算法中特征映射的准确性的问题,为了进一步提高图像超分辨率重建的的质量,文中提出一种基于卷积稀疏自编码的图像超分辨率重建算法。该算法首先在预训练阶段利用稀疏自编码器对输入高低分辨率图像分别进行训练,得到对应的图像稀疏特征表示;然后再由卷积神经网络根据得到的稀疏系数共同训练相应的滤波器及特征映射函数并更新到最优解;最后由高分辨率滤波器和对应的稀疏表示系数卷积求和,得到高分辨率重建图像估计。实验结果显示,改进算法的峰值信噪比(PSNR)结果较卷积稀疏编码算法提高了近0.1 dB,有效提高了重建图像的质量。

For the accuracy of feature maps in convolutional sparse coding algorithm, in order to further improve the quality of image super-resolution reconstruction, an image super-resolution(SR) reconstruction algorithm based on convolutional sparse auto-encoder was proposed in this paper. In this algorithm,firstly, the input images were pre-trained with sparse auto-encoder for obtaining the feature of LR and HR image;after that, the convolutional neural network trained the corresponding filters and feature mapping function and updated to the optimal solution according to the obtained sparse coefficients;finally, the summation of the convolutions of high-resolution(HR) filters and the corresponding feature maps could reconstruct the HR image. The experimental results show that the peak signal-to-noise ratio(PSNR) of the proposed algorithm is nearly 0.1 d B higher than the CSC algorithm, which improves the quality of reconstructed images.