Weizhe Sun
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Weizhe Sun has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Dong Li
School of Airspace Science and Engineering, Shandong University
1 shared publication - Fei Yang
School of Economics and Management, Southwest Jiaotong University
1 shared publication - Haoran Lu
School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264200, China
1 shared publication - Lei Zhang
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
1 shared publication - Ran Ke
School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264200, China
1 shared publication - Zhen Liu
School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264200, China
1 shared publication
Latest Publications
- Open Access
Enhanced Feature Refinement Network Based on Depthwise Separable Convolution for Lightweight Image Super-Resolution
Image super-resolution (SR) techniques aim to enhance the clarity and realism of images. Recently, a wide range of excellent SR algorithms with powerful characterization capabilities have emerged and are widely used. However, there are still challenges and room for improvement in designing a lighter and more edge-friendly SR networks for hardware devices. In this paper, we propose a lightweight enhanced feature refinement network (EFRN) based on depthwise separatable convolution for SR reconstruction. The core network components consist of multiple enhanced feature refinement blocks (EFRB), which fully fuse channel features to extract more accurate low-frequency information based on the attention of different channels. In addition, a lightweight residual block (LRB) and a lightweight dual attention block (LDAB) are designed to enhance network information extraction with minimal parameter cost. We improve the feature refinement by using 1 × 1 convolution instead of a channel selection operation to reduce the dimensionality of the features and extract the refined features more efficiently. Finally, to achieve better reconstruction performance, the depth and number of channels of the network are expanded while keeping the total number of parameters at a low level. Extensive experiments have been conducted to demonstrate the superiority of our EFRN over other mainstream SR algorithms in terms of reconstruction results and the number of parameters.
Open AccessEnhanced Feature Refinement Network Based on Depthwise Separable Convolution for Lightweight Image Super-Resolution
Image super-resolution (SR) techniques aim to enhance the clarity and realism of images. Recently, a wide range of excellent SR algorithms with powerful characterization capabilities have emerged and are widely used. However, there are still challenges and room for improvement in designing a lighter and more edge-friendly SR networks for hardware devices. In this paper, we propose a lightweight enhanced feature refinement network (EFRN) based on depthwise separatable convolution for SR reconstruction. The core network components consist of multiple enhanced feature refinement blocks (EFRB), which fully fuse channel features to extract more accurate low-frequency information based on the attention of different channels. In addition, a lightweight residual block (LRB) and a lightweight dual attention block (LDAB) are designed to enhance network information extraction with minimal parameter cost. We improve the feature refinement by using 1 × 1 convolution instead of a channel selection operation to reduce the dimensionality of the features and extract the refined features more efficiently. Finally, to achieve better reconstruction performance, the depth and number of channels of the network are expanded while keeping the total number of parameters at a low level. Extensive experiments have been conducted to demonstrate the superiority of our EFRN over other mainstream SR algorithms in terms of reconstruction results and the number of parameters.