  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.
