183,577 research outputs found
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network
Methods based on convolutional neural network (CNN) have demonstrated
tremendous improvements on single image super-resolution. However, the previous
methods mainly restore images from one single area in the low resolution (LR)
input, which limits the flexibility of models to infer various scales of
details for high resolution (HR) output. Moreover, most of them train a
specific model for each up-scale factor. In this paper, we propose a
multi-scale super resolution (MSSR) network. Our network consists of
multi-scale paths to make the HR inference, which can learn to synthesize
features from different scales. This property helps reconstruct various kinds
of regions in HR images. In addition, only one single model is needed for
multiple up-scale factors, which is more efficient without loss of restoration
quality. Experiments on four public datasets demonstrate that the proposed
method achieved state-of-the-art performance with fast speed
Deep Model-Based Super-Resolution with Non-uniform Blur
We propose a state-of-the-art method for super-resolution with non-uniform
blur. Single-image super-resolution methods seek to restore a high-resolution
image from blurred, subsampled, and noisy measurements. Despite their
impressive performance, existing techniques usually assume a uniform blur
kernel. Hence, these techniques do not generalize well to the more general case
of non-uniform blur. Instead, in this paper, we address the more realistic and
computationally challenging case of spatially-varying blur. To this end, we
first propose a fast deep plug-and-play algorithm, based on linearized ADMM
splitting techniques, which can solve the super-resolution problem with
spatially-varying blur. Second, we unfold our iterative algorithm into a single
network and train it end-to-end. In this way, we overcome the intricacy of
manually tuning the parameters involved in the optimization scheme. Our
algorithm presents remarkable performance and generalizes well after a single
training to a large family of spatially-varying blur kernels, noise levels and
scale factors
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