Image Deblurring and Super-Resolution Using Deep Convolutional Neural Networks. Albluwi, F., Krylov, V. A., & Dahyot, R. In 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP), pages 1-6, Sept, 2018. Github: https://github.com/Fatma-Albluwi/DBSRCNN
Image Deblurring and Super-Resolution Using Deep Convolutional Neural Networks [pdf]Paper  doi  abstract   bibtex   1 download  
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.

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