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Learning a Mixture of Deep Networks for Single Image Super-Resolution

  • Ding LiuEmail author
  • Zhaowen Wang
  • Nasser Nasrabadi
  • Thomas Huang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10113)

Abstract

Single image super-resolution (SR) is an ill-posed problem which aims to recover high-resolution (HR) images from their low-resolution (LR) observations. The crux of this problem lies in learning the complex mapping between low-resolution patches and the corresponding high-resolution patches. Prior arts have used either a mixture of simple regression models or a single non-linear neural network for this propose. This paper proposes the method of learning a mixture of SR inference modules in a unified framework to tackle this problem. Specifically, a number of SR inference modules specialized in different image local patterns are first independently applied on the LR image to obtain various HR estimates, and the resultant HR estimates are adaptively aggregated to form the final HR image. By selecting neural networks as the SR inference module, the whole procedure can be incorporated into a unified network and be optimized jointly. Extensive experiments are conducted to investigate the relation between restoration performance and different network architectures. Compared with other current image SR approaches, our proposed method achieves state-of-the-arts restoration results on a wide range of images consistently while allowing more flexible design choices.

Keywords

Sparse Code Bicubic Interpolation Convolutional Layer Inference Module Inference Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ding Liu
    • 1
    Email author
  • Zhaowen Wang
    • 2
  • Nasser Nasrabadi
    • 3
  • Thomas Huang
    • 1
  1. 1.Beckman InstituteUniversity of Illinois at Urbana-ChampaignUrbanaUSA
  2. 2.Adobe ResearchSan JoseUSA
  3. 3.Lane Department of Computer Science and Electrical EngineeringWest Virginia UniversityMorgantownUSA

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