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Soft-IntroVAE for Continuous Latent space Image Super-Resolution

arXiv 2307.09008 ↗First posted 18 Jul 2023eess.IV cs.CVcited by 0

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Abstract (arXiv)

Continuous image super-resolution (SR) recently receives a lot of attention from researchers, for its practical and flexible image scaling for various displays. Local implicit image representation is one of the methods that can map the coordinates and 2D features for latent space interpolation. Inspired by Variational AutoEncoder, we propose a Soft-introVAE for continuous latent space image super-resolution (SVAE-SR). A novel latent space adversarial training is achieved for photo-realistic image restoration. To further improve the quality, a positional encoding scheme is used to extend the original pixel coordinates by aggregating frequency information over the pixel areas. We show the effectiveness of the proposed SVAE-SR through quantitative and qualitative comparisons, and further, illustrate its generalization in denoising and real-image super-resolution.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 18 Jul 2023.

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References · 18

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  5. 2018Image Super-Resolution Using Very Deep Residual Channel Attention Networkscited by 1,256
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  7. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
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  12. 2020Single Image Super-Resolution via a Holistic Attention Networkcited by 265
6 more
  1. 2019Texture Fields: Learning Texture Representations in Function Spacecited by 210
  2. 2018Residual Dense Network for Image Restorationcited by 181
  3. 2019Meta-SR: A Magnification-Arbitrary Network for Super-Resolutioncited by 180
  4. 2020Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencodercited by 23
  5. 2019Image Super-Resolution via Attention based Back Projection Networkscited by 16
  6. 2020Unsupervised Real Image Super-Resolution via Generative Variational AutoEncodercited by 16

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