Image super-resolution reconstruction based on multi-scale feature loss function

Abstract

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.