references notes

Lightweight image super-resolution with enhanced CNN

@article{TIAN2020106235,
title = {Lightweight image super-resolution with enhanced CNN},
journal = {Knowledge-Based Systems},
volume = {205},
pages = {106235},
year = {2020},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2020.106235},
url = {https://www.sciencedirect.com/science/article/pii/S0950705120304391},
author = {Chunwei Tian and Ruibin Zhuge and Zhihao Wu and Yong Xu and Wangmeng Zuo and Chen Chen and Chia-Wen Lin},
keywords = {Image super-resolution, CNN, Lightweight enhanced network, Enhancement and compression, Information refinement},
abstract = {Deep convolutional neural networks (CNNs) with strong expressive ability have achieved impressive performances on single image super-resolution (SISR). However, their excessive amounts of convolutions and parameters usually consume high computational cost and more memory storage for training a SR model, which limits their applications to SR with resource-constrained devices in real world. To resolve these problems, we propose a lightweight enhanced SR CNN (LESRCNN) with three successive sub-blocks, an information extraction and enhancement block (IEEB), a reconstruction block (RB) and an information refinement block (IRB). Specifically, the IEEB extracts hierarchical low-resolution (LR) features and aggregates the obtained features step-by-step to increase the memory ability of the shallow layers on deep layers for SISR. To remove redundant information obtained, a heterogeneous architecture is adopted in the IEEB. After that, the RB converts low-frequency features into high-frequency features by fusing global and local features, which is complementary with the IEEB in tackling the long-term dependency problem. Finally, the IRB uses coarse high-frequency features from the RB to learn more accurate SR features and construct a SR image. The proposed LESRCNN can obtain a high-quality image by a model for different scales. Extensive experiments demonstrate that the proposed LESRCNN outperforms state-of-the-arts on SISR in terms of qualitative and quantitative evaluation. The code of LESRCNN is accessible on https://github.com/hellloxiaotian/LESRCNN.}
}