Single and Multi-Modal Image Super-Resolution
In single image super-resolution, one aims to reconstruct a high-resolution version of an image from its low-resolution version. In the multimodal case, one can also use data from different imaging modalities as side information.
Dictionary Learning Approaches for Image Super-Resolution
Cambridge University, March 2020
- J. Huang and P.L. Dragotti, Learning Deep Analysis Dictionaries for Image Super-Resolution, IEEE Transactions on Signal Processing, vol. 68, pages: 6633 - 6648, 2020
- X.Deng and P.L. Dragotti,
Deep Convolutional Neural
Network for Multi-modal Image Restoration and Fusion,
IEEE Transactions on Pattern Analysis and Machine
Intelligence, April 2020.
- X. Deng, Ren Yang, Mai Xu and P.L. Dragotti, Wavelet Domain Style Transfer for an Effective Perception-Distortion Tradeoff in Single Image Super-Resolution, ICCV 2019, Seoul, Korea, November 2019, video
- X. Deng and P.L. Dragotti,
Deep Coupled ISTA Network
for Multi-modal Image Super-Resolution, IEEE
Transactions on Image Processing, to appear. Software
- X.Deng et al. RADAR:
Robust Algorithm for Depth Image Super Resolution Based on
FRI Theory and Multimodal Dictionary Learning, IEEE
Transactions on Circuits and Systems for Video Technology,
to appear 2019
- X. Wei and P. L. Dragotti, FRESH -FRI-based single image super-resolution algorithm, IEEE Trans on Image Processing, Vol.25(8), pp. 3723-3735, August 2016.