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TMSR: Tiny Multi-path CNNs for Super Resolution

arXiv 2312.01644 ↗First posted 4 Dec 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

In this paper, we proposed a tiny multi-path CNN-based Super-Resolution (SR) method, called TMSR. We mainly refer to some tiny CNN-based SR methods, under 5k parameters. The main contribution of the proposed method is the improved multi-path learning and self-defined activated function. The experimental results show that TMSR obtains competitive image quality (i.e. PSNR and SSIM) compared to the related works under 5k parameters.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 14 references that are in the map. First posted on arXiv 4 Dec 2023.

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References · 8

  1. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  2. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,540
  3. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,199
  4. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  5. 2016Accelerating the Super-Resolution Convolutional Neural Networkcited by 393
  6. 2020Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategycited by 66
  7. 2021Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devicescited by 30
  8. 2022A survey on recently proposed activation functions for Deep Learningcited by 5

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