在图像超分辨率重建问题中, 许多基于深度学习的方法大多采用传统的均方误差 (MSE)作为损失函数, 重建后的图像容易出现细节模糊和过于平滑的问题。针对这一问题, 本文对传统的均方误差损失函数进行改进, 提出一种基于多尺度特征损失函数的图像超分辨率重建方法。整个网络模型由基于 DenseNet的重建模型和一个用来优化多尺度特征损失函数的卷积神经网络串联构成。将重建后得到的图像和对应的原始高清图像作为串联的卷积神经网络的输入, 计算重建图像卷积得到的不同尺度特征图与对应的原始高清图像卷积得到的不同尺度特征图的均方误差。实验结果表明, 本文提出的方法在主观视觉效果和 PSRN、SSIM上均有所提升。

In the image super-resolution reconstruction, many methods based on deep learning mostly adopt the traditional mean squared error (MSE) as the loss function, and the reconstructed image is prone to the problem of fuzzy details and too smooth. In order to solve this problem, this paper improves the traditional mean square error loss function and proposes an image super-resolution reconstruction method based on multi-scale feature loss function. The whole network model consists of a DenseNet-based reconstruction model and a convolutional neural network which is used to optimize the multi-scale feature loss function. Taking the reconstructed image and the corresponding original HD image as the input of the convolved neural network in series, the mean square error of the different scale feature images obtained by convolution of the reconstructed image with the corresponding original HD image was calculated. Experimental results show that the method in this paper is improved in subjective vision, PSRN and SSIM.