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The 2018 PIRM Challenge on Perceptual Image Super-resolution

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arxiv 1809.07517 v3 pith:NITMNWQA submitted 2018-09-20 cs.CV

Yochai Blau , Roey Mechrez , Radu Timofte , Tomer Michaeli , Lihi Zelnik-Manor This is my paper · ORCID
classification cs.CV
keywords perceptualimagepirmalgorithmschallengehumanmethodsopinion
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This paper reports on the 2018 PIRM challenge on perceptual super-resolution (SR), held in conjunction with the Perceptual Image Restoration and Manipulation (PIRM) workshop at ECCV 2018. In contrast to previous SR challenges, our evaluation methodology jointly quantifies accuracy and perceptual quality, therefore enabling perceptual-driven methods to compete alongside algorithms that target PSNR maximization. Twenty-one participating teams introduced algorithms which well-improved upon the existing state-of-the-art methods in perceptual SR, as confirmed by a human opinion study. We also analyze popular image quality measures and draw conclusions regarding which of them correlates best with human opinion scores. We conclude with an analysis of the current trends in perceptual SR, as reflected from the leading submissions.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SROBB: Targeted Perceptual Loss for Single Image Super-Resolution

    cs.CV 2019-08 conditional novelty 6.0 of 10

    A targeted perceptual loss that applies edge-specific and texture-specific VGG features to different image regions produces super-resolved images users prefer.

  2. RankSRGAN: Generative Adversarial Networks with Ranker for Image Super-Resolution

    cs.CV 2019-08 conditional novelty 6.0 of 10

    RankSRGAN uses a learned ranking network as a differentiable surrogate for perceptual metrics, enabling GAN-based super-resolution to be optimized directly toward NIQE, Ma, or PI.

  3. Super-resolution of Omnidirectional Images Using Adversarial Learning

    cs.CV 2019-08 conditional novelty 6.0 of 10

    A GAN-based super-resolution method for omnidirectional images, using a PatchGAN discriminator and a 360-SS loss, improves spherical quality metrics over SRGAN and bicubic.

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