mlsanityData to 14 Oct 2026

Perception-Oriented Stereo Image Super-Resolution

arXiv 2207.06617 ↗First posted 14 Jul 2022eess.IV cs.CVcited by 4

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Abstract (arXiv)

Recent studies of deep learning based stereo image super-resolution (StereoSR) have promoted the development of StereoSR. However, existing StereoSR models mainly concentrate on improving quantitative evaluation metrics and neglect the visual quality of super-resolved stereo images. To improve the perceptual performance, this paper proposes the first perception-oriented stereo image super-resolution approach by exploiting the feedback, provided by the evaluation on the perceptual quality of StereoSR results. To provide accurate guidance for the StereoSR model, we develop the first special stereo image super-resolution quality assessment (StereoSRQA) model, and further construct a StereoSRQA database. Extensive experiments demonstrate that our StereoSR approach significantly improves the perceptual quality and enhances the reliability of stereo images for disparity estimation.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 20 of its 43 references that are in the map. First posted on arXiv 14 Jul 2022.

Builds on

20 of 43 references.

References · 20

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
  4. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,908
  5. 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
  6. 2018ESRGAN: Enhanced Super-Resolution Generative Adversarial Networkscited by 1,254
  7. 2014Image Super-Resolution Using Deep Convolutional Networkscited by 1,224
  8. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 903
  9. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 881
  10. 2017Learning a Single Convolutional Super-Resolution Network for Multiple Degradationscited by 228
  11. 2020PIPAL: a Large-Scale Image Quality Assessment Dataset for Perceptual Image Restorationcited by 178
  12. 2019Feedback Network for Image Super-Resolutioncited by 155
8 more
  1. 2017RankIQA: Learning from Rankings for No-reference Image Quality Assessmentcited by 151
  2. 2016Learning a No-Reference Quality Metric for Single-Image Super-Resolutioncited by 144
  3. 2018Fast and Accurate Single Image Super-Resolution via Information Distillation Networkcited by 121
  4. 2020Structure-Preserving Super Resolution with Gradient Guidancecited by 116
  5. 2020Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolutioncited by 90
  6. 2018StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Predictioncited by 88
  7. 2019Learning Parallax Attention for Stereo Image Super-Resolutioncited by 56
  8. 2014MCL-3D: a database for stereoscopic image quality assessment using 2D-image-plus-depth sourcecited by 4

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2024Learning Accurate and Enriched Features for Stereo Image Super-Resolutioncited by 3
  2. 2025StereoINR: Cross-View Geometry Consistent Stereo Super Resolution with Implicit Neural Representationcited by 3
  3. 2023Toward Real World Stereo Image Super-Resolution via Hybrid Degradation Model and Discriminator for Implied Stereo Image Informationcited by 2
  4. 2025Deep Learning Empowered Super-Resolution: A Comprehensive Survey and Future Prospectscited by 1