A total variation-based algorithm for pixel-level image fusion

IEEE Transactions on Image Processing

Volumn 18, Issue 9, 2009, Pages 2137-2143

  Kumar, Mrityunjay ^a   Dass, Sarat ^b  

^a EASTMAN KODAK COMPANY   (United States)

^b MICHIGAN STATE UNIVERSITY   (United States)

Author keywords

Eigenvector; Forward model; Image fusion; Inverse problem; Pixel level fusion; Total variation (TV)

Indexed keywords

AFFINE MODEL; COMPUTED TOMOGRAPHY; EIGENVECTOR; FORWARD MODEL; FORWARD MODELS; FUSED IMAGES; INFRA-RED SENSOR; MULTIPLE SENSORS; PIXEL-LEVEL FUSION; PIXEL-LEVEL IMAGE FUSION; SEMINORMS; TOTAL VARIATION; TOTAL VARIATION (TV);

ALGORITHMS; COMPUTERIZED TOMOGRAPHY; EIGENVALUES AND EIGENFUNCTIONS; INVERSE PROBLEMS; MAGNETIC RESONANCE IMAGING; PIXELS; PRINCIPAL COMPONENT ANALYSIS; SENSORS; TOMOGRAPHY;

IMAGE FUSION;

ALGORITHM; BRAIN; COMPUTER ASSISTED TOMOGRAPHY; FACTUAL DATABASE; IMAGE PROCESSING; LETTER; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PRINCIPAL COMPONENT ANALYSIS; REGRESSION ANALYSIS; SCINTISCANNING; THEORETICAL MODEL;

ALGORITHMS; BRAIN; DATABASES, FACTUAL; IMAGE PROCESSING, COMPUTER-ASSISTED; LEAST-SQUARES ANALYSIS; MAGNETIC RESONANCE IMAGING; MODELS, THEORETICAL; PRINCIPAL COMPONENT ANALYSIS; TOMOGRAPHY, X-RAY COMPUTED;

EID: 69349091164     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2009.2025006     Document Type: Article

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