Enhanced dual branches network for arbitrary‐scale image super‐resolution

Deep convolutional neural networks (CNNs) are of great improvement for single image super‐resolution (SISR). However, most existing SISR pre‐trained models can only perform single image restoration and the upscale factors cannot be non‐integers, which limits its application in real‐world scenarios. In this letter, an enhanced dual branches network (EDBNet) in upsampling network is proposed to generate arbitrary‐scale super‐resolution (SR) images. Specifically, the authors design a scale‐guidance upsampling module (SGU) by adding the scale factors and pixel‐level features to guide the weights of convolution. The SGU module performs discriminant learning for each instance in the same batch. Extensive experiments on four benchmark datasets show that the proposed method can achieve superior SR results. In this letter, the authors propose a enhanced dual branches network (EDBNet) to fuse pixel feature and scale information to generate arbitrary‐scale SR images in upsampling network. Specifically, a scale‐guidance upsampling module (SGU) is designed by adding the scale factors and pixel‐level features to guide the weights of convolution. The SGU module performs discriminant learning for each instance in the same batch.