Yi-Wei Lin
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Yi-Wei Lin has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Che-Cheng Chang
Department of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, Taiwan
1 shared publication - Po-Jui Pan
Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan
1 shared publication - Wen-Pin Chen
Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan
1 shared publication - Yu-Jhan Lin
Department of Information Engineering and Computer Science, Feng Chia University, Taichung 407, Taiwan
1 shared publication
Latest Publications
- Open Access
- 28 April 2025
- Published by MDPI AG in Engineering Proceedings
- Vol. 92 (1) , 27
Real-Time Super Resolution Utilizing Dilation and Depthwise Separable Convolution
Computer vision applications require high-quality reproductions of original images, typically demanding complex models with many trainable parameters and floating-point operations. This increases computational load and restricts deployment on resource-constrained devices. Therefore, we designed a new dilation depthwise super-resolution (DDSR) model that is composed of dilation convolution, depthwise separable convolution, and residual connection, to overcome the predicaments. Compared with the well-known model, fast super-resolution convolutional neural network (FSRCNN), the developed DDSR shows better performance in evaluations and You Only Look Once (YOLO v8) confidence scores. Most importantly, the architecture of the developed DDSR has 55% trainable parameters, 19% floating-point operations per second (FLOPs) of one-channel FSRCNN, 27% of the trainable parameters, and 8% of the FLOPs of three-channel FSRCNN.
- 28 April 2025
- Published by MDPI AG in Engineering Proceedings
- Vol. 92 (1) , 27
Open AccessReal-Time Super Resolution Utilizing Dilation and Depthwise Separable Convolution
Computer vision applications require high-quality reproductions of original images, typically demanding complex models with many trainable parameters and floating-point operations. This increases computational load and restricts deployment on resource-constrained devices. Therefore, we designed a new dilation depthwise super-resolution (DDSR) model that is composed of dilation convolution, depthwise separable convolution, and residual connection, to overcome the predicaments. Compared with the well-known model, fast super-resolution convolutional neural network (FSRCNN), the developed DDSR shows better performance in evaluations and You Only Look Once (YOLO v8) confidence scores. Most importantly, the architecture of the developed DDSR has 55% trainable parameters, 19% floating-point operations per second (FLOPs) of one-channel FSRCNN, 27% of the trainable parameters, and 8% of the FLOPs of three-channel FSRCNN.