A 1-Lipschitz deep image prior and an averaged NLM denoiser are added to a low-rank plus sparse HSI inpainting framework to claim fixed-point convergence, but the proof relies on a strong convexity assumption that does not hold for masked data.
Fast hyperspectral image denoising and inpainting based on low-rank and sparse representations
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Self-supervised Deep Hyperspectral Inpainting with the Plug and Play and Deep Image Prior Models
A 1-Lipschitz deep image prior and an averaged NLM denoiser are added to a low-rank plus sparse HSI inpainting framework to claim fixed-point convergence, but the proof relies on a strong convexity assumption that does not hold for masked data.