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


(32x, 16x, 8x, 4x and 2x SR from a single SR model)

Abstract
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.

Papers

CVPR17
Journal extension
Citation

Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang, "Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution", in IEEE Conference on Computer Vision and Pattern Recognition, 2017.

Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang, "Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks", arXiv:1710.01992, 2017.


Bibtex
@inproceedings{LapSRN,
    author    = {Lai, Wei-Sheng and Huang, Jia-Bin and Ahuja, Narendra and Yang, Ming-Hsuan}, 
    title     = {Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution}, 
    booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
    year      = {2017}
}
@article{MSLapSRN,
    author    = {Lai, Wei-Sheng and Huang, Jia-Bin and Ahuja, Narendra and Yang, Ming-Hsuan}, 
    title     = {Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks}, 
    journal   = {arXiv:1710.01992},
    year      = {2017}
}
Download

Code (Github)
Include both LapSRN and MS-LapSRN

Training Datasets (86.2 MB)
T91, BSDS200, General100

Testing Datasets (303 MB)
Set5, Set14, BSDS100, Urban100, Manga109, Historical

Network Architecture

Results
Benchmark

Runtime v.s. performance on Urban100
#Parameters v.s. performance on Urban100





References