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Efficient Cost-and-Quality Controllable Arbitrary-scale Super-resolution with Fourier Constraints

arXiv 2510.23978 ↗First posted 28 Oct 2025cs.CVcited by 0

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Abstract (arXiv)

Cost-and-Quality (CQ) controllability in arbitrary-scale super-resolution is crucial. Existing methods predict Fourier components one by one using a recurrent neural network. However, this approach leads to performance degradation and inefficiency due to independent prediction. This paper proposes predicting multiple components jointly to improve both quality and efficiency.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 33 references that are in the map. First posted on arXiv 28 Oct 2025.

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25 of 33 references.

References · 25

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  2. 2015Distilling the Knowledge in a Neural Networkcited by 10,076
  3. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,291
  4. 2015Learning both Weights and Connections for Efficient Neural Networkscited by 1,998
  5. 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 1,979
  6. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,543
  7. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
  8. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,218
  9. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  10. 2016XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networkscited by 829
  11. 2017Deep Mutual Learningcited by 621
  12. 2015BinaryConnect: Training Deep Neural Networks with binary weights during propagationscited by 571
13 more
  1. 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 550
  2. 2018Deep Back-Projection Networks For Super-Resolutioncited by 224
  3. 2016FractalNet: Ultra-Deep Neural Networks without Residualscited by 216
  4. 2023A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendationscited by 200
  5. 2019Meta-SR: A Magnification-Arbitrary Network for Super-Resolutioncited by 180
  6. 2021Local Texture Estimator for Implicit Representation Functioncited by 126
  7. 2022A Comprehensive Survey on Model Quantization for Deep Neural Networks in Image Classificationcited by 64
  8. 2018Task-Driven Super Resolution: Object Detection in Low-resolution Imagescited by 52
  9. 2021SRWarp: Generalized Image Super-Resolution under Arbitrary Transformationcited by 26
  10. 2023Linearized Relative Positional Encodingcited by 17
  11. 2019Deep Back-Projection Networks for Single Image Super-resolutioncited by 16
  12. 2023Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Imagescited by 7
  13. 2024Test-time Cost-and-Quality Controllable Arbitrary-Scale Super-Resolution with Variable Fourier Componentscited by 1

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