Bilateral Upsampling Network for Single Image Super-Resolution With Arbitrary Scaling Factors.

Abstract

Single Image Super-Resolution (SISR) is essential for many computer vision tasks. In some real-world applications, such as object recognition and image classification, the captured image size can be arbitrary while the required image size is fixed, which necessitates SISR with arbitrary scaling factors. It is a challenging problem to take a single model to accomplish the SISR task under arbitrary scaling factors. To solve that problem, this paper proposes a bilateral upsampling network which consists of a bilateral upsampling filter and a depthwise feature upsampling convolutional layer. The bilateral upsampling filter is made up of two upsampling filters, including a spatial upsampling filter and a range upsampling filter. With the introduction of the range upsampling filter, the weights of the bilateral upsampling filter can be adaptively learned under different scaling factors and different pixel values. The output of the bilateral upsampling filter is then provided to the depthwise feature upsampling convolutional layer, which upsamples the low-resolution (LR) feature map to the high-resolution (HR) feature space depthwisely and well recovers the structural information of the HR feature map. The depthwise feature upsampling convolutional layer can not only efficiently reduce the computational cost of the weight prediction of the bilateral upsampling filter, but also accurately recover the textual details of the HR feature map. Experiments on benchmark datasets demonstrate that the proposed bilateral upsampling network can achieve better performance than some state-of-the-art SISR methods.