Journal: Scientific Data

Article Title: A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution

doi: 10.1038/s41597-023-02538-9

Figure Lengend Snippet: Reconstruction accuracy obtained for our MuS2 benchmark measured with PSNR (in dB), SSIM, LPIPS, and the balanced score \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathscr{B}}$$\end{document} B , obtained for different interpolation techniques alongside HighRes-net and RAMS networks trained using real-world and simulated images.

Article Snippet: However, it can be noticed that these qualitative observations are quantitatively reflected only in the LPIPS values, while PSNR and SSIM are slightly worse for HighRes-net and RAMS–overall, both LPIPS and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathscr{B}}$$\end{document} B indicate that SR networks perform better than interpolation and they penalize for the artefacts.

Techniques:

Journal: Scientific Data

Article Title: A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution

doi: 10.1038/s41597-023-02538-9

Figure Lengend Snippet: Reconstruction outcome (band B08) obtained with RAMS and HighRes-net trained from real-life PROBA-V NIR and Red images and from simulated data, compared with image interpolation techniques and HR reference. PSNR (in dB), SSIM, LPIPS and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathscr{B}}$$\end{document} B scores are presented above each example. The images present an area of 5.5 × 6.5 km near Wageningen, Netherlands–the WV-2 image was acquired in June 2011, and S-2 images were acquired in April 2019–March 2021.

Article Snippet: However, it can be noticed that these qualitative observations are quantitatively reflected only in the LPIPS values, while PSNR and SSIM are slightly worse for HighRes-net and RAMS–overall, both LPIPS and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathscr{B}}$$\end{document} B indicate that SR networks perform better than interpolation and they penalize for the artefacts.

Techniques:

Journal: Scientific Data

Article Title: A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution

doi: 10.1038/s41597-023-02538-9

Figure Lengend Snippet: The MOS survey outcome showing how often each metric was consistent with the answers (in %), stratified into SR networks trained with PROBA-V and simulated images, and interpolation.

Article Snippet: However, it can be noticed that these qualitative observations are quantitatively reflected only in the LPIPS values, while PSNR and SSIM are slightly worse for HighRes-net and RAMS–overall, both LPIPS and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathscr{B}}$$\end{document} B indicate that SR networks perform better than interpolation and they penalize for the artefacts.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network

doi: 10.3390/s20102970

Figure Lengend Snippet: Comparison of the CPSNR, SSIM and FSIMc values for the different CFA patterns used with the proposed method on the Kodak image dataset.

Article Snippet: , Kodak , SSIM-R , 0.8253 , 0.5630 , 0.5372 , 0.5405 , 0.6490 , 0.7927 , 0.8300.

Techniques: Comparison

Journal: Sensors (Basel, Switzerland)

Article Title: Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network

doi: 10.3390/s20102970

Figure Lengend Snippet: Comparison of the SSIM values among the various demosaicing methods on the Kodak and the McMaster image datasets.

Article Snippet: , Kodak , SSIM-R , 0.8253 , 0.5630 , 0.5372 , 0.5405 , 0.6490 , 0.7927 , 0.8300.

Techniques: Comparison