ViSIR: Vision Transformer Single Image Reconstruction Method for Earth System Models

• URL: http://arxiv.org/abs/2502.06741v2
• Date: Tue, 11 Feb 2025 16:02:23 GMT
• Title: ViSIR: Vision Transformer Single Image Reconstruction Method for Earth System Models
• Authors: Ehsan Zeraatkar, Salah Faroughi, Jelena Tešić,
• Abstract summary: Earth system models (ESMs) integrate the interactions of the atmosphere, ocean, land, ice, and biosphere to estimate the state of regional and global climate. We propose the Vision Transformer Sinusoidal Representation Networks (ViSIR) to improve the single image SR (SR) reconstruction task for the ESM data.
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• Abstract: Purpose: Earth system models (ESMs) integrate the interactions of the atmosphere, ocean, land, ice, and biosphere to estimate the state of regional and global climate under a wide variety of conditions. The ESMs are highly complex, and thus, deep neural network architectures are used to model the complexity and store the down-sampled data. In this paper, we propose the Vision Transformer Sinusoidal Representation Networks (ViSIR) to improve the single image SR (SR) reconstruction task for the ESM data. Methods: ViSIR combines the SR capability of Vision Transformers (ViT) with the high-frequency detail preservation of the Sinusoidal Representation Network (SIREN) to address the spectral bias observed in SR tasks. Results: The ViSIR outperforms ViT by 4.1 dB, SIREN by 7.5 dB, and SR-Generative Adversarial (SR-GANs) by 7.1dB PSNR on average for three different measurements. Conclusion: The proposed ViSIR is evaluated and compared with state-of-the-art methods. The results show that the proposed algorithm is outperforming other methods in terms of Mean Square Error(MSE), Peak-Signal-to-Noise-Ratio(PSNR), and Structural Similarity Index Measure(SSIM).

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