Compressive hyper-spectral imaging in the presence of impulse noise

Autor:	Angshul Majumdar, Hemant Kumar Aggarwal, Snigdha Tariyal
Rok vydání:	2015
Předmět:	medicine.medical_specialty Noise measurement business.industry Hyperspectral imaging Impulse noise Spectral imaging Gradient noise symbols.namesake Gaussian noise symbols medicine Image noise Computer vision Value noise Artificial intelligence business Algorithm Mathematics
Zdroj:	WHISPERS
DOI:	10.1109/whispers.2015.8075396
Popis:	There have been a number of studies for addressing the recovery of compressively sampled hyper-spectral images in the presence of Gaussian noise; this work proposes a recovery technique in the presence of impulse noise. Owing to the sparse nature of the impulse noise, the data fidelity term is an L1-norm. The hyper-spectral datacube is modeled as a combination of sparse transform coefficients and a low-rank matrix. The resulting optimization problem is solved using the split-Bregman approach. The success of the proposed method is shown using the numerical results and visual evaluation.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0c4649bf619c7234efc35f1f413e132a https://doi.org/10.1109/whispers.2015.8075396 Zobrazit plný text záznamu