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- W4387329811 abstract "Diffusion learning is a generative technique commonly applied to create new images or audio directly from sampled noise. The machine learning approach works by applying degrading signals, such as noise, continuously and learning the denoising process with a neural network. In place of noise, other operations can be performed, such as the addition of atmosphere effects using a physics-based radiative transport code. In this paper, we explore coupling the MODTRAN software to a diffusion learning framework. The goal is to apply atmosphere systematically for a variety of reflective surfaces and use diffusion learning to train models for atmospheric correction. To achieve this, we generate a scoped dataset containing randomized Lambertian surfaces with differing solar illumination and surface angles." @default.
- W4387329811 created "2023-10-05" @default.
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- W4387329811 date "2023-10-04" @default.
- W4387329811 modified "2023-10-14" @default.
- W4387329811 title "Diffusion learning for atmospheric correction" @default.
- W4387329811 doi "https://doi.org/10.1117/12.2677771" @default.
- W4387329811 hasPublicationYear "2023" @default.
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