14,351 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
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution
We propose methodologies to train highly accurate and efficient deep
convolutional neural networks (CNNs) for image super resolution (SR). A cascade
training approach to deep learning is proposed to improve the accuracy of the
neural networks while gradually increasing the number of network layers. Next,
we explore how to improve the SR efficiency by making the network slimmer. Two
methodologies, the one-shot trimming and the cascade trimming, are proposed.
With the cascade trimming, the network's size is gradually reduced layer by
layer, without significant loss on its discriminative ability. Experiments on
benchmark image datasets show that our proposed SR network achieves the
state-of-the-art super resolution accuracy, while being more than 4 times
faster compared to existing deep super resolution networks.Comment: Accepted to IEEE Winter Conf. on Applications of Computer Vision
(WACV) 2018, Lake Tahoe, US
Image-to-Image Translation with Conditional Adversarial Networks
We investigate conditional adversarial networks as a general-purpose solution
to image-to-image translation problems. These networks not only learn the
mapping from input image to output image, but also learn a loss function to
train this mapping. This makes it possible to apply the same generic approach
to problems that traditionally would require very different loss formulations.
We demonstrate that this approach is effective at synthesizing photos from
label maps, reconstructing objects from edge maps, and colorizing images, among
other tasks. Indeed, since the release of the pix2pix software associated with
this paper, a large number of internet users (many of them artists) have posted
their own experiments with our system, further demonstrating its wide
applicability and ease of adoption without the need for parameter tweaking. As
a community, we no longer hand-engineer our mapping functions, and this work
suggests we can achieve reasonable results without hand-engineering our loss
functions either.Comment: Website: https://phillipi.github.io/pix2pix/, CVPR 201
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