基于改进卷积神经网络的单幅图像超分辨率重建方法

对于重建图像存在的边缘失真和纹理细节信息模糊的问题,提出一种基于改进卷积神经网络(CNN)的图像超分辨率重建方法。首先在底层特征提取层以三种插值方法和五种锐化方法进行多种预处理操作,并将只进行一次插值操作的图像和先进行一次插值后进行一次锐化的图像合并排列成三维矩阵;然后在非线性映射层将预处理后构成的三维特征映射作为深层残差网络的多通道输入,以获取更深层次的纹理细节信息;最后在重建层为减少图像重建时间在网络结构中引入亚像素卷积来完成图像重建操作。在多个常用数据集上的实验结果表明,与经典方法相比,所提方法重建图像的纹理细节信息和高频信息能得到更好的恢复,峰值信噪比(PSNR)平均增加0.23 dB,结构相似性(SSIM)平均增加0.006 6。在保证图像重建时间的前提下,所提方法更好地保持重建图像的纹理细节并减少图像边缘失真,提升重建图像的性能。

Single image super-resolution reconstruction method based on improved convolutional neural network

Aiming at the problem of edge distortion and fuzzy texture detail information in reconstructed images, an image super-resolution reconstruction method based on improved Convolutional Neural Network (CNN) was proposed. Firstly, various preprocessing operations were performed on the underlying feature extraction layer by three interpolation methods and five sharpening methods, and the images which were only subjected to one interpolation operation and the images which were sharpened after interpolation operation were arranged into a 3D matrix. Then, the 3D feature map formed by the preprocessing was used as the multi-channel input of a deep residual network in the nonlinear mapping layer to obtain deeper texture detail information. Finally, for reducing image reconstruction time, sub-pixel convolution was introduced into the reconstruction layer to complete image reconstruction operation. Experimental results on several common datasets show that the proposed method achieves better restored texture detail information and high-frequency information in the reconstructed image compared with the classical methods. Furthermore, the Peak Signal-to-Noise Ratio (PSNR) was increased by 0.23 dB on average, and the structural similarity was increased by 0.0066 on average. The proposed method can better maintain the texture details of the reconstructed image and reduce the image edge distortion under the premise of ensuring the image reconstruction time, improving the performance of image reconstruction.