PIRM2018 Challenge on Spectral Image Super-Resolution: Methods and Results

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This paper introduces a newly collected and novel dataset (StereoMSI) for example-based single and colour-guided spectral image super-resolution. The dataset was first released and promoted during the PIRM2018 spectral image super-resolution challenge. To the best of our knowledge, the dataset is the first of its kind, comprising 350 registered colour-spectral image pairs. The dataset has been used for the two tracks of the challenge and, for each of these, we have provided a split into training, validation and testing. This arrangement is a result of the challenge structure and phases, with the first track focusing on examplebased spectral image super-resolution and the second one aiming at exploiting the registered stereo colour imagery to improve the resolution of the spectral images. Each of the tracks and splits has been selected to be consistent across a number of image quality metrics. The dataset is quite general in nature and can be used for a wide variety of applications in addition to the development of spectral image super-resolution methods.

Section: Bicubic Upsampling Metricsmentioning

confidence: 99%

Section: Bicubic Upsampling Metricsmentioning

confidence: 99%

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PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study

Shoeiby

Robles-Kelly

Wei

et al. 2019

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“…The majority of the current challenges related to AI and deep learning for image restoration and enhancement [32,35,3,4,6,28] are primarily targeting only one goal -high quantitative results measured by mean square error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), mean opinion score (MOS) and other similar metrics. As a result, the general recipe for achieving top results in these competitions is quite similar: more layers/filters, deeper architectures and longer training on dozens of GPUs.…”

Section: Introductionmentioning

confidence: 99%

PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report

Ignatov

Timofte

et al. 2019

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This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tracks. In the first one, participants were solving the classical image super-resolution problem with a bicubic downscaling factor of 4. The second track was aimed at real-world photo enhancement, and the goal was to map low-quality photos from the iPhone 3GS device to the same photos captured with a DSLR camera. The target metric used in this challenge combined the runtime, PSNR scores and solutions' perceptual results measured in the user study. To ensure the efficiency of the submitted models, we additionally measured their runtime and memory requirements on Android smartphones. The proposed solutions significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones. * A. Ignatov and R. Timofte ({andrey,radu.timofte}@vision.ee.ethz.ch, ETH Zurich) are the challenge organizers, while the other authors participated in the challenge. The Appendix contains the authors' teams and affiliations. PIRM 2018 Challenge webpage: http://ai-benchmark.org

“…In this paper, we address spatial image super-resolution for spectral images. We tackle the problem posed by the PIRM2018 Spectral Image Challenge [20,19] for reconstructing high-resolution spectral images from twice (LR2) and thrice (LR3) downscaled versions. The challenge has two tracks.…”

Section: Introductionmentioning

confidence: 99%

Multi-modal Spectral Image Super-Resolution

Lahoud

Zhou

Süsstrunk

2019

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Recent advances have shown the great power of deep convolutional neural networks (CNN) to learn the relationship between low and high-resolution image patches. However, these methods only take a single-scale image as input and require large amount of data to train without the risk of overfitting. In this paper, we tackle the problem of multi-modal spectral image super-resolution while constraining ourselves to a small dataset. We propose the use of different modalities to improve the performance of neural networks on the spectral superresolution problem. First, we use multiple downscaled versions of the same image to infer a better high-resolution image for training, we refer to these inputs as a multi-scale modality. Furthermore, color images are usually taken at a higher resolution than spectral images, so we make use of color images as another modality to improve the super-resolution network. By combining both modalities, we build a pipeline that learns to super-resolve using multi-scale spectral inputs guided by a color image. Finally, we validate our method and show that it is economic in terms of parameters and computation time, while still producing state-of-the-art results. 1