Redundancy (engineering)

Blueprint

Mathematics

Convolution (computer science)

Operating system

Algorithm

Mathematical optimization

Computer engineering

Engineering

Residual

Enhanced Data Rates for GSM Evolution

Mathematical analysis

Artificial intelligence

Set (abstract data type)

Programming language

Code (set theory)

Computer science

Separable space

Theoretical computer science

Artificial neural network

Mechanical engineering

Convolutional neural network

Recent advances in single image super-resolution (SISR) have achieved extraordinary performance, but the computational cost is too heavy to apply in edge devices. To alleviate this problem, many novel and effective solutions have been proposed. Convolutional neural network (CNN) with the attention mechanism has attracted increasing attention due to its efficiency and effectiveness. However, there is still redundancy in the convolution operation. In this paper, we propose Blueprint Separable Residual Network (BSRN) containing two efficient designs. One is the usage of blueprint separable convolution (BSConv), which takes place of the redundant convolution operation. The other is to enhance the model ability by introducing more effective attention modules. The experimental results show that BSRN achieves state-of-the-art performance among existing efficient SR methods. Moreover, a smaller variant of our model BSRN-S won the first place in model complexity track of NTIRE 2022 Efficient SR Challenge. The code is available at https://github.com/xiaom233/BSRN.

Blueprint Separable Residual Network for Efficient Image Super-Resolution