Lightweight Attended Multi-Scale Residual Network for Single Image Super-Resolution

Recently, deep convolutional neural networks (CNN) have been widely applied in the single image super-resolution (SISR) task and achieved significant progress in reconstruction performance. However, most of the existing CNN-based SR models are impractical to real-world applicants due to numerous parameters and heavy computation. To tackle this issue, we propose a lightweight attended multi-scale residual network (LAMRN) in this work. Specially, we present an attended multi-scale residual block (AMSRB) to extract multi-scale features, where we embed the efficient channel attention block (ECA) to enhance the discrimination of features. Besides, we introduce a double-attention fusion (DAF) block to fuse the low-level and high-level features efficiently. We use spatial attention and channel attention to obtain guidance from the low-level and high-level features, which is used to guide the feature fusion. Extensive experimental results demonstrate that our LAMRN achieves competitive performance against the state-of-the-art methods with similar parameters and computational operations.