@INPROCEEDINGS{8516983, 
author =  {F. Albluwi and V. A. Krylov and R. Dahyot},
booktitle =  {2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)}, 
title =  {Image Deblurring and Super-Resolution Using Deep Convolutional Neural Networks}, 
year =  {2018},
volume =  {}, 
number =  {}, 
pages =  {1-6},
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 lowresolution 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 stateof-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.},
note = {Github: https://github.com/Fatma-Albluwi/DBSRCNN},
keywords =  {convolution;image reconstruction;image resolution;image restoration;learning (artificial intelligence);neural nets;image deblurring;deep convolutional neural networks;multiple high performance algorithms;high-resolution images;low-resolution image input;deep learning algorithms;low-resolution images;deep learning approach;blurred low resolution images;super-resolution convolutional neural network;Training;Image resolution;Pipelines;Image reconstruction;Signal resolution;Feature extraction;Convolutional neural networks;Image super-resolution;deblurring;deep learning;convolutional neural networks}, 
doi =  {10.1109/MLSP.2018.8516983},
url={https://mural.maynoothuniversity.ie/15254/1/RD_image.pdf},
ISSN =  {1551-2541}, month =  {Sept}}

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The related problem of super-resolution from blurred or corrupted lowresolution 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. 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Albluwi and V. A. Krylov and R. Dahyot},\nbooktitle = {2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP)}, \ntitle = {Image Deblurring and Super-Resolution Using Deep Convolutional Neural Networks}, \nyear = {2018},\nvolume = {}, \nnumber = {}, \npages = {1-6},\nabstract = {Recently multiple high performance algorithms have been developed to infer high-resolution images from low-resolution\nimage input using deep learning algorithms. The related\nproblem of super-resolution from blurred or corrupted lowresolution images has however received much less attention.\nIn this work, we propose a new deep learning approach that\nsimultaneously addresses deblurring and super-resolution\nfrom blurred low resolution images. 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