Communication Dans Un Congrès Année : 2022

Hyperspectral Super-Resolution by Unsupervised Convolutional Neural Network and Sure

Résumé

Recent advances in deep learning (DL) reveal that the structure of a convolutional neural network (CNN) is a good image prior (called deep image prior (DIP)), bridging the model-based and DL-based methods in image restoration. However, optimizing a DIP-based CNN is prone to over-fitting leading to a poorly reconstructed image. This paper derives a loss function based on Stein's unbiased risk estimate (SURE) for unsupervised training of a DIP-based CNN applied to the hyperspectral image (HSI) super-resolution. The SURE loss function is an unbiased estimate of the mean-square-error (MSE) between the clean low-resolution image and the low-resolution estimated image, which relies only on the observed low-resolution image. Experimental results on HSI show that the proposed method not only improves the performance, but also avoids overfitting. Codes are available at https://github.com/hvn2/SURE-MS-HS
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Dates et versions

hal-04880399 , version 1 (10-01-2025)

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Citer

Han Nguyen, Magnus Ulfarsson, Johannes Sveinsson, Mauro Dalla Mura. Hyperspectral Super-Resolution by Unsupervised Convolutional Neural Network and Sure. IGARSS 2022 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2022, Kuala Lumpur, Malaysia. pp.903-906, ⟨10.1109/igarss46834.2022.9883576⟩. ⟨hal-04880399⟩
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