Image Super-Resolution Algorithm based on V-transform Combined with Neural Network
Nan Nan, Shijie Yue, Ruipeng Gang, Chenghua Li, Ruixia Song · 2022
Single image super-resolution aims to increase the size and the visual effectiveness of a low-resolution image. Although existing deep learning-based methods have achieved promising results, they still face great challenges in dealing with the reconstruction of complicated scenes. This is mainly because the deep convolution operations cannot balance the low frequency contents and high frequency details. To mitigate this problem, we propose VTSR, an image super-resolution method based on the V-transform. In VTSR, we propose a VT-block. Change the previous practice of using a convolution layer to directly increase the network dimension to , while the spatial and frequency domain information is parallel, providing richer information for subsequent deep convolution neural networks. Then, we introduce the V-transform, a wavelet-transform, to the super-resolution task to extract richer frequency information. Finally, we designed a loss function mainly for frequency domain information, which has a positive impact on the super-resolution effect and training. Experiments show that the combination of our proposed method and V-transform can achieve better results than most state-of-the-art methods.