mlsp18
F. Albluwi, V. A. Krylov, R. Dahyot. "Image Deblurring And Super-Resolution Using Deep Convolutional Neural Networks",
IEEE International Workshop on Machine Learning for Signal Processing 2018,
Proc. of IEEE MLSP 2018, Aalborg (Denmark), September 17-20, 2018.
Abstract
Recently multiple high performance algorithms have been developed to infer high-resolution images from low-resolution image input using deep learning algorithms. The related problem of super-resolution from blurred or corrupted low-resolution images has however received much less attention. In this work, we propose a new deep learning approach that simultaneously addresses deblurring and super-resolution from blurred low resolution images. We evaluate the state-of-the-art super-resolution convolutional neural network (SRCNN) architecture proposed in [1] for the blurred reconstruction scenario and propose a revised deeper architecture that proves its superiority experimentally both when the levels of blur are known and unknown a priori.
Bibtex
@INPROCEEDINGS{KrylovMLSP18,
author = {Fatma Albluwi and Vladimir A. Krylov and Rozenn Dahyot},
title = {Image Deblurring And Super-Resolution Using Deep Convolutional Neural Networks},
year = {2018},
booktitle = {Proc. of IEEE MLSP},
address = {Aalborg, Denmark}
}