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ELSR: Extreme Low-Power Super Resolution Network For Mobile Devices

arXiv 2208.14600 ↗First posted 31 Aug 2022cs.CV eess.IVcited by 0

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Abstract (arXiv)

With the popularity of mobile devices, e.g., smartphone and wearable devices, lighter and faster model is crucial for the application of video super resolution. However, most previous lightweight models tend to concentrate on reducing lantency of model inference on desktop GPU, which may be not energy efficient in current mobile devices. In this paper, we proposed Extreme Low-Power Super Resolution (ELSR) network which only consumes a small amount of energy in mobile devices. Pretraining and finetuning methods are applied to boost the performance of the extremely tiny model. Extensive experiments show that our method achieves a excellent balance between restoration quality and power consumption. Finally, we achieve a competitive score of 90.9 with PSNR 27.34 dB and power 0.09 W/30FPS on the target MediaTek Dimensity 9000 plantform, ranking 1st place in the Mobile AI & AIM 2022 Real-Time Video Super-Resolution Challenge.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 15 references that are in the map. First posted on arXiv 31 Aug 2022.

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References · 12

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,224
  3. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,204
  4. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 903
  5. 2019EDVR: Video Restoration with Enhanced Deformable Convolutional Networkscited by 494
  6. 2021BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignmentcited by 342
  7. 2020BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyondcited by 304
  8. 2019Lightweight Image Super-Resolution with Information Multi-distillation Networkcited by 227
  9. 2018Frame-Recurrent Video Super-Resolutioncited by 160
  10. 2020Residual Feature Distillation Network for Lightweight Image Super-Resolutioncited by 122
  11. 2020Efficient Image Super-Resolution Using Pixel Attentioncited by 87
  12. 2021edge-SR: Super-Resolution For The Massescited by 9

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