Learned Multimodal Convolutional Sparse Coding for Guided Image Super-Resolution

The success of deep learning in various tasks, including solving inverse problems, has triggered the need for designing deep neural networks that incorporate domain knowledge. In this paper, we design a multimodal deep learning architecture for guided image super-resolution, which refers to the prob...

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Vydané v:Proceedings - International Conference on Image Processing s. 2891 - 2895
Hlavní autori: Marivani, Iman, Tsiligianni, Evaggelia, Cornelis, Bruno, Deligiannis, Nikos
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 01.09.2019
Predmet:
Convolution
Convolutional codes
convolutional sparse coding
Deep learning
Dictionaries
Guided image super-resolution
Image coding
Image resolution
multimodal deep neural networks
Neural networks
ISSN:2381-8549
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Popis
Shrnutí:The success of deep learning in various tasks, including solving inverse problems, has triggered the need for designing deep neural networks that incorporate domain knowledge. In this paper, we design a multimodal deep learning architecture for guided image super-resolution, which refers to the problem of super-resolving a low-resolution image with the aid of a high-resolution image of another modality. The proposed architecture is based on a novel deep learning model, obtained by unfolding a proximal method that solves the problem of convolutional sparse coding with side information. We applied the proposed architecture to super-resolve near-infrared images using RGB images as side information. Experimental results report average PSNR gains of up to 2.85 dB against state-of-the-art multimodal deep learning and sparse coding models.
ISSN:2381-8549
DOI:10.1109/ICIP.2019.8803313