SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution

• URL: http://arxiv.org/abs/2310.00413v1
• Date: Sat, 30 Sep 2023 15:23:30 GMT
• Title: SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution
• Authors: Gengchen Mai, Ni Lao, Weiwei Sun, Yuchi Ma, Jiaming Song, Chenlin Meng, Hongxu Ma, Jinmeng Rao, Ziyuan Li, Stefano Ermon
• Abstract summary: We propose a neural implicit model that represents an image as a function of both continuous pixel coordinates in the spatial domain and continuous wavelengths in the spectral domain. We show that SSIF generalizes well to both unseen spatial resolutions and spectral resolutions. It can generate high-resolution images that improve the performance of downstream tasks by 1.7%-7%.
• Score: 73.46167948298041
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Existing digital sensors capture images at fixed spatial and spectral resolutions (e.g., RGB, multispectral, and hyperspectral images), and each combination requires bespoke machine learning models. Neural Implicit Functions partially overcome the spatial resolution challenge by representing an image in a resolution-independent way. However, they still operate at fixed, pre-defined spectral resolutions. To address this challenge, we propose Spatial-Spectral Implicit Function (SSIF), a neural implicit model that represents an image as a function of both continuous pixel coordinates in the spatial domain and continuous wavelengths in the spectral domain. We empirically demonstrate the effectiveness of SSIF on two challenging spatio-spectral super-resolution benchmarks. We observe that SSIF consistently outperforms state-of-the-art baselines even when the baselines are allowed to train separate models at each spectral resolution. We show that SSIF generalizes well to both unseen spatial resolutions and spectral resolutions. Moreover, SSIF can generate high-resolution images that improve the performance of downstream tasks (e.g., land use classification) by 1.7%-7%.

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