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 ne...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/92/1/27 |
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| Summary: | 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. |
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| ISSN: | 2673-4591 |