22,465 research outputs found
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
Patch-based Progressive 3D Point Set Upsampling
We present a detail-driven deep neural network for point set upsampling. A
high-resolution point set is essential for point-based rendering and surface
reconstruction. Inspired by the recent success of neural image super-resolution
techniques, we progressively train a cascade of patch-based upsampling networks
on different levels of detail end-to-end. We propose a series of architectural
design contributions that lead to a substantial performance boost. The effect
of each technical contribution is demonstrated in an ablation study.
Qualitative and quantitative experiments show that our method significantly
outperforms the state-of-the-art learning-based and optimazation-based
approaches, both in terms of handling low-resolution inputs and revealing
high-fidelity details.Comment: accepted to cvpr2019, code available at https://github.com/yifita/P3
Deep Networks for Image Super-Resolution with Sparse Prior
Deep learning techniques have been successfully applied in many areas of
computer vision, including low-level image restoration problems. For image
super-resolution, several models based on deep neural networks have been
recently proposed and attained superior performance that overshadows all
previous handcrafted models. The question then arises whether large-capacity
and data-driven models have become the dominant solution to the ill-posed
super-resolution problem. In this paper, we argue that domain expertise
represented by the conventional sparse coding model is still valuable, and it
can be combined with the key ingredients of deep learning to achieve further
improved results. We show that a sparse coding model particularly designed for
super-resolution can be incarnated as a neural network, and trained in a
cascaded structure from end to end. The interpretation of the network based on
sparse coding leads to much more efficient and effective training, as well as a
reduced model size. Our model is evaluated on a wide range of images, and shows
clear advantage over existing state-of-the-art methods in terms of both
restoration accuracy and human subjective quality
- …