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LSR: A Light-Weight Super-Resolution Method

arXiv 2302.13596 ↗First posted 27 Feb 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

A light-weight super-resolution (LSR) method from a single image targeting mobile applications is proposed in this work. LSR predicts the residual image between the interpolated low-resolution (ILR) and high-resolution (HR) images using a self-supervised framework. To lower the computational complexity, LSR does not adopt the end-to-end optimization deep networks. It consists of three modules: 1) generation of a pool of rich and diversified representations in the neighborhood of a target pixel via unsupervised learning, 2) selecting a subset from the representation pool that is most relevant to the underlying super-resolution task automatically via supervised learning, 3) predicting the residual of the target pixel via regression. LSR has low computational complexity and reasonable model size so that it can be implemented on mobile/edge platforms conveniently. Besides, it offers better visual quality than classical exemplar-based methods in terms of PSNR/SSIM measures.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 26 references that are in the map. First posted on arXiv 27 Feb 2023.

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References · 11

  1. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
  2. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  3. 2020Pre-Trained Image Processing Transformercited by 891
  4. 2018Residual Dense Network for Image Super-Resolutioncited by 796
  5. 2019Meta-SR: A Magnification-Arbitrary Network for Super-Resolutioncited by 180
  6. 2021ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristiccited by 81
  7. 2018Interpretable Convolutional Neural Networks via Feedforward Designcited by 49
  8. 2016Understanding Convolutional Neural Networks with A Mathematical Modelcited by 33
  9. 2022Green Learning: Introduction, Examples and Outlookcited by 30
  10. 2022On Supervised Feature Selection from High Dimensional Feature Spacescited by 23
  11. 2017On Data-Driven Saak Transformcited by 22

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