mlsanityData to 13 Oct 2026

GDCA: GAN-based single image super resolution with Dual discriminators and Channel Attention

arXiv 2111.05014 ↗First posted 9 Nov 2021eess.IV cs.AI cs.CVcited by 0

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Abstract (arXiv)

Single Image Super-Resolution (SISR) is a very active research field. This paper addresses SISR by using a GAN-based approach with dual discriminators and incorporating it with an attention mechanism. The experimental results show that GDCA can generate sharper and high pleasing images compare to other conventional methods.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 5 of its 8 references that are in the map. First posted on arXiv 9 Nov 2021.

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5 of 8 references.

References · 5

  1. 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,906
  2. 2016Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Networkcited by 1,202
  3. 2015Accurate Image Super-Resolution Using Very Deep Convolutional Networkscited by 902
  4. 2021Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics Modelcited by 14
  5. 2021Robust MAML: Prioritization task buffer with adaptive learning process for model-agnostic meta-learningcited by 8

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