Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks

Wei Sheng Lai, Jia Bin Huang, Narendra Ahuja, Ming Hsuan Yang

Research output: Contribution to journalArticlepeer-review


Convolutional neural networks have recently demonstrated high-quality reconstruction for single image super-resolution. However, existing methods often require a large number of network parameters and entail heavy computational loads at runtime for generating high-accuracy super-resolution results. In this paper, we propose the deep Laplacian Pyramid Super-Resolution Network for fast and accurate image super-resolution. The proposed network progressively reconstructs the sub-band residuals of high-resolution images at multiple pyramid levels. In contrast to existing methods that involve the bicubic interpolation for pre-processing (which results in large feature maps), the proposed method directly extracts features from the low-resolution input space and thereby entails low computational loads. We train the proposed network with deep supervision using the robust Charbonnier loss functions and achieve high-quality image reconstruction. Furthermore, we utilize the recursive layers to share parameters across as well as within pyramid levels, and thus drastically reduce the number of parameters. Extensive quantitative and qualitative evaluations on benchmark datasets show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of run-time and image quality.

Original languageEnglish (US)
Article number8434354
Pages (from-to)2599-2613
Number of pages15
JournalIEEE transactions on pattern analysis and machine intelligence
Issue number11
StatePublished - Nov 1 2019


  • Laplacian pyramid
  • Single-image super-resolution
  • deep convolutional neural networks

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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