Lightweight subpixel sampling network for image super-resolution

Recently, convolutional neural networks (CNNs) have facilitated the rapid development of image super-resolution. Most deep networks are challenging to apply to the real world due to their high cost of memory storage and computational complexity. This paper addresses this issue by proposing a lightweight subpixel sampling network (SSN). Specifically, we use a traditional encoder-decoder structure and replace the deconvolution and pooling layers by subpixel up-sampling and down-sampling without parameters. Subpixel sampling retains more image information than other sampling methods. In addition, we propose parsimonious spatial and channel attention blocks through which multi-scale features are fused and more image textures can be recovered. Through extensive experiments, we validate the effectiveness of subpixel sampling, spatial attention block, and channel attention block. In terms of quantitative metrics and visual quality, our models achieve performance comparable to state-of-the-art methods.