A Cost-Effective Interpolation for Multi-Magnification Super-Resolution

Kuan Yu Huang, Suraj Pramanik, Pei Yin Chen

Research output: Contribution to journalArticlepeer-review


Super-Resolution (SR) was an important research topic, and SR methods based on Convolutional Neural Network (CNN) confirmed its groundbreaking performance. However, notably implementing the CNN model into resource-limited hardware devices is a great challenge. Therefore, we present a hardware-friendly and low-cost interpolation for Multi-Magnification SR image reconstruction. We follow our previous work, which is a learning-based interpolation (LCDI) with a self-defined classifier of image texture, and extend its original × 2 architecture to × 3 and × 4 architecture. Besides, the required pre-trained weights are reduced by the fusion scheme. Experimentally, the proposed method has only 75% lower pre-trained weights than LCDI. Compared to the related work OLM-SI (One linear learning mapping-SI), the run-time and quantity of pre-trained weights of the × 2 proposed method are at least 90% lower. Compared to CNN-based SR methods, the proposed method loses a little lower performance, but the evaluation of computational cost is much lower. In conclusion, the proposed method is cost-effective and a practical solution for resource-limited hardware and device.

Original languageEnglish
Pages (from-to)102076-102086
Number of pages11
JournalIEEE Access
Publication statusPublished - 2022

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)
  • Electrical and Electronic Engineering


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