Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods

Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods. February, 2018.

Traditionally, analytical methods have been used to solve imaging problems such as image restoration, inpainting, and superresolution (SR). In recent years, the fields of machine and deep learning have gained a lot of momentum in solving such imaging problems, often surpassing the performance provided by analytical approaches.

@misc{noauthor_using_2018,
	title = {Using {Deep} {Neural} {Networks} for {Inverse} {Problems} in {Imaging}: {Beyond} {Analytical} {Methods}},
	shorttitle = {Using {Deep} {Neural} {Networks} for {Inverse} {Problems} in {Imaging}},
	url = {https://signalprocessingsociety.org/publications-resources/ieee-signal-processing-magazine/using-deep-neural-networks-inverse-problems},
	abstract = {Traditionally, analytical methods have been used to solve imaging problems such as image restoration, inpainting, and superresolution (SR). In recent years, the fields of machine and deep learning have gained a lot of momentum in solving such imaging problems, often surpassing the performance provided by analytical approaches.},
	language = {en},
	urldate = {2019-11-19},
	journal = {IEEE Signal Processing Society},
	month = feb,
	year = {2018},
	keywords = {***}
}

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