Journal
IEEE TRANSACTIONS ON MULTIMEDIA
Volume 21, Issue 12, Pages 3106-3121Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2019.2919431
Keywords
Single image super-resolution; deep learning; neural networks; objective function
Categories
Funding
- National Natural Science Foundation of China [61471216, 61771276]
- National Key Research and Development Program of China [2016YFB0101001]
- Special Foundation for the Development of Strategic Emerging Industries of Shenzhen [JCYJ20170307153940960, JCYJ20170817161845824]
Ask authors/readers for more resources
Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that aims to obtain a high-resolution output from one of its low-resolution versions. Recently, powerful deep learning algorithms have been applied to SISR and have achieved state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods and group them into two categories according to their contributions to two essential aspects of SISR: The exploration of efficient neural network architectures for SISR and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is first established, and several critical limitations of the baseline are summarized. Then, representative works on overcoming these limitations are presented based on their original content, as well as our critical exposition and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally, we conclude this review with some current challenges and future trends in SISR that leverage deep learning algorithms.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available