Self-Supervised Deep Hyperspectral Inpainting with Plug-and-Play and Deep Image Prior Models
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 d...
Saved in:
Main Authors: | Shuo Li, Mehrdad Yaghoobi |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Remote Sensing |
Subjects: | |
Online Access: | https://www.mdpi.com/2072-4292/17/2/288 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Error-Mask-Adaptive Dynamic Filtering for Image Inpainting
by: Keunsoo Ko, et al.
Published: (2025-01-01) -
<inline-formula><tex-math notation="LaTeX">$\mathrm{D}^{3}$</tex-math></inline-formula>T: Deep Denoising Dictionary Tensor for Hyperspectral Anomaly Detection
by: Qiangqiang Shen, et al.
Published: (2025-01-01) -
Unsupervised Hyperspectral Denoising Based on Deep Image Prior and Least Favorable Distribution
by: Keivan Faghih Niresi, et al.
Published: (2022-01-01) -
Onboard Processing of Hyperspectral Imagery: Deep Learning Advancements, Methodologies, Challenges, and Emerging Trends
by: Nafiseh Ghasemi, et al.
Published: (2025-01-01) -
Liquid-based cytological diagnosis of pancreatic neuroendocrine tumors using hyperspectral imaging and deep learning
by: Taojing Ran, et al.
Published: (2025-03-01)