10 papers
Hyperspectral Diffusion Equivariant Imaging (HyDiff-EI): A Self-supervised Framework for Hyperspectral Image Inpainting
Shuo Li, Mike Davies, Mehrdad Yaghoobi
A novel Hyperspectral diffusion Equivariant Imaging (HyDiff-EI) framework for solving the hyperspectral image (HSI) inpainting problem has been presented here. Unlike conventional…
Self-supervised Deep Hyperspectral Inpainting with the Plug and Play and Deep Image Prior Models
Shuo Li, Mehrdad Yaghoobi
Hyperspectral images are typically composed of hundreds of narrow and contiguous spectral bands, each containing information regarding the material composition of the imaged scene.…
Equivariant Imaging for Self-supervised Hyperspectral Image Inpainting
Shuo Li, Mike Davies, Mehrdad Yaghoobi
Hyperspectral imaging (HSI) is a key technology for earth observation, surveillance, medical imaging and diagnostics, astronomy and space exploration. The conventional technology f…
On the Transferability of Large-Scale Self-Supervision to Few-Shot Audio Classification
Calum Heggan, Sam Budgett, Timothy Hospedales +1
In recent years, self-supervised learning has excelled for its capacity to learn robust feature representations from unlabelled data. Networks pretrained through self-supervision s…
Self-Supervised Hyperspectral Inpainting with the Optimisation inspired Deep Neural Network Prior
Shuo Li, Mehrdad Yaghoobi
Hyperspectral Image (HSI)s cover hundreds or thousands of narrow spectral bands, conveying a wealth of spatial and spectral information. However, due to the instrumental errors and…
Self-supervised Deep Hyperspectral Inpainting with the Sparsity and Low-Rank Considerations
Shuo Li, Mehrdad Yaghoobi
Hyperspectral images are typically composed of hundreds of narrow and contiguous spectral bands, each containing information about the material composition of the imaged scene. How…