Deep Learning for Single Image Super-Resolution: A Brief Review

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…

• NN
  National Natural Science Foundation of China

  Awards: 61471216, 61771276
• NK
  National Key Research and Development Program of China

  Award: 2016YFB0101001
• SF
  Special Foundation for the Development of Strategic Emerging Industries of Shenzhen

  Awards: JCYJ20170817161845824, JCYJ20170307153940960