The 2018 PIRM Challenge on Perceptual Image Super-Resolution

By Yochai Blau, Roey Mechrez, Radu Timofte, Tomer Michaeli, Lihi Zelnik-Manor. 2018

94 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

The Perception-Distortion Tradeoff (2017, methodology): “We measure perceptual quality using the NR metric NIQE [36], which was shown to correlate well with human opinion scores in a recent SR challenge [64] (see Appendix H for experiments with the NR metrics BRISQUE [30], BLIINDS-II [35] and the recent NR metric by Ma et al.”
SinGAN: Learning a Generative Model From a Single Natural Image (2019, methodology): “Following [4], we compare these 5 methods in Table 3 on the BSD100 dataset [35] in terms of distortion (RMSE) and perceptual quality (NIQE [40]), which are two fundamentally conflicting requirements [5].”
Residual Non-local Attention Networks for Image Restoration (2019, background): “Recently, great progresses have been made in image restoration community, where Timofte et al. (Timofte et al., 2017), Ancuti et al. (Ancuti et al., 2018), and Blau et al. (Blau et al., 2018) lead the main competitions recently and achieved new research status and records.”
Deep Learning for Image Super-Resolution: A Survey (2019, methodology): “PIRM [47] 200 (617, 482) 292, 021 PNG environments, flora, natural scenery, objects, people, etc.”
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications (2020, unknown): “[211] and obtained the best perceptual index.”
A Deep Journey into Super-resolution (2019, background): “The next challenge for super-resolution is the Perceptual Image Restoration and Manipulation2 (PIRM) [94].”
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff (2019, unknown): “In image restoration scenarios, such a 2 -fold increase in the MSE ( 3 dB decrease in PSNR) has been shown to enable a substantial improvement in perceptual quality by practical algorithms (Blau et al., 2018; Ledig et al., 2017).”