Self-Supervised Deep Hyperspectral Inpainting with Plug-and-Play and Deep Image Prior Models

Self-Supervised Deep Hyperspectral Inpainting with Plug-and-Play and Deep Image Prior Models

Blog Article

Hyperspectral images are typically composed of hundreds of narrow and contiguous spectral bands, each containing information regarding the material composition of the imaged scene.However, these images can be affected by various sources of noise, distortions, or data loss, which can significantly degrade their quality and usefulness.This paper introduces NEFF C17MS32H0B N90 Built In 60cm Electric Single Oven Stainless Steel a convergent guaranteed algorithm, LRS-PnP-DIP(1-Lip), which successfully addresses the instability issue of DHP that has been reported before.The proposed algorithm extends the successful joint low-rank and sparse model to Clear RP Parts further exploit the underlying data structures beyond the conventional and sometimes restrictive unions of subspace models.

A stability analysis guarantees the convergence of the proposed algorithm under mild assumptions, which is crucial for its application in real-world scenarios.Extensive experiments demonstrate that the proposed solution consistently delivers visually and quantitatively superior inpainting results, establishing state-of-the-art performance.