Recursive Multi-Stage Upscaling Network with Discriminative Fusion for Super-Resolution
Yue Lu, Zhuqing Jiang, Guodong Ju, Liangheng Shen, Aidong Men · 2019
Since convolutional neural networks have fundamentally changed how computers learn features, many super-resolution (SR) methods focus on extracting informative features to improve performance by using more layers or more innovative skip-connections. However, using more layers in feature extraction while keeping the upscaling module unchanged will exacerbate structural imbalances. Besides, blindly fusing different stage features by concatenation may make them interfere with each other. To address these issues, we proposed a recursive multi-stage upscaling network (RMUN) with discriminative fusion module (DFM). Specifically, we construct multiple upscaling paths to produce various high-resolution features in the forward propagation and deliver error loss in the back propagation. Furthermore, we fuse and re-weight those features by DFM to avoid mutual interference and boost reconstruction quality. Experiments show that RMUN is superior to the state-of-the-art methods, especially for large scale SR tasks.