Multi-Modal Image Super-Resolution via Deep Convolutional Transform Learning

Real-world situations often involve processing data from diverse imaging modalities like Multispectral (MS), Near Infrared (NIR), and RGB, each capturing different aspects of the same scene. These modalities often vary in spatial and spectral resolution. Hence, Multi-modal Image Super-Resolution (MI...

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Vydáno v:	2024 32nd European Signal Processing Conference (EUSIPCO) s. 671 - 675
Hlavní autoři:	Kumar, Kriti, Majumdar, Angshul, Kumar, A Anil, Chandra, M Girish
Médium:	Konferenční příspěvek
Jazyk:	angličtina
Vydáno:	European Association for Signal Processing - EURASIP 26.08.2024
Témata:	Convolution Convolutional codes Convolutional sparse coding Convolutional transform learning Deep models Encoding Image reconstruction Imaging Joint optimization Multimodal image super-resolution Optimization Spatial resolution Superresolution Training data Transforms
ISSN:	2076-1465
On-line přístup:	Získat plný text
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