mlsanityData to 7 Oct 2026

Unsupervised HDR Imaging: What Can Be Learned from a Single 8-bit Video?

arXiv 2202.05522 ↗First posted 11 Feb 2022cs.GR cs.CV eess.IVcited by 2

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Abstract (arXiv)

Recently, Deep Learning-based methods for inverse tone-mapping standard dynamic range (SDR) images to obtain high dynamic range (HDR) images have become very popular. These methods manage to fill over-exposed areas convincingly both in terms of details and dynamic range. Typically, these methods, to be effective, need to learn from large datasets and to transfer this knowledge to the network weights. In this work, we tackle this problem from a completely different perspective. What can we learn from a single SDR video? With the presented zero-shot approach, we show that, in many cases, a single SDR video is sufficient to be able to generate an HDR video of the same quality or better than other state-of-the-art methods.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 41 references that are in the map. First posted on arXiv 11 Feb 2022.

Builds on

15 of 41 references.

References · 15

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  12. 2021NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Resultscited by 36
3 more
  1. 2018Image Correction via Deep Reciprocating HDR Transformationcited by 30
  2. 2019Single-frame Regularization for Temporally Stable CNNscited by 21
  3. 2018Compression of High Dynamic Range Video Using the HEVC and H.264/AVC Standardscited by 4

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Cited by year

Citing papers per year: 2022: 2.

2 papers.

  1. 2022GlowGAN: Unsupervised Learning of HDR Images from LDR Images in the Wildcited by 21
  2. 2022LHDR: HDR Reconstruction for Legacy Content using a Lightweight DNNcited by 6