Single image super-resolution based on deep learning and gradient transformation
Jingxu Chen, Xiaohai He, Honggang Chen, Qizhi Teng, Linbo Qing · 2016
In this paper, an effective single image super-resolution method based on deep learning and gradient transformation is proposed. Firstly, the low-resolution image is upscaled by convolutional neural network. Then we calculate the gradients of the upscaled image, and transform them into desired gradients by using gradient transformation network. The transformed gradients are utilized as a constraint to establish the reconstruction energy function. Finally, we optimize this energy function to estimate the high-resolution image. Experimental results show that our proposed algorithm can produce sharp high-resolution images with few ringing or jaggy artifacts, and our results have high values of the objective assessment parameters.