17,202 research outputs found
Efficient Residual Dense Block Search for Image Super-Resolution
Although remarkable progress has been made on single image super-resolution
due to the revival of deep convolutional neural networks, deep learning methods
are confronted with the challenges of computation and memory consumption in
practice, especially for mobile devices. Focusing on this issue, we propose an
efficient residual dense block search algorithm with multiple objectives to
hunt for fast, lightweight and accurate networks for image super-resolution.
Firstly, to accelerate super-resolution network, we exploit the variation of
feature scale adequately with the proposed efficient residual dense blocks. In
the proposed evolutionary algorithm, the locations of pooling and upsampling
operator are searched automatically. Secondly, network architecture is evolved
with the guidance of block credits to acquire accurate super-resolution
network. The block credit reflects the effect of current block and is earned
during model evaluation process. It guides the evolution by weighing the
sampling probability of mutation to favor admirable blocks. Extensive
experimental results demonstrate the effectiveness of the proposed searching
method and the found efficient super-resolution models achieve better
performance than the state-of-the-art methods with limited number of parameters
and FLOPs
Deep Learning for Single Image Super-Resolution: A Brief Review
Single image super-resolution (SISR) is a notoriously challenging ill-posed
problem, which aims to obtain a high-resolution (HR) output from one of its
low-resolution (LR) versions. To solve the SISR problem, recently powerful deep
learning algorithms have been employed and achieved the state-of-the-art
performance. In this survey, we review representative deep learning-based SISR
methods, and group them into two categories according to their major
contributions to two essential aspects of SISR: the exploration of efficient
neural network architectures for SISR, and the development of effective
optimization objectives for deep SISR learning. For each category, a baseline
is firstly established and several critical limitations of the baseline are
summarized. Then representative works on overcoming these limitations are
presented based on their original contents as well as our critical
understandings and analyses, and relevant comparisons are conducted from a
variety of perspectives. Finally we conclude this review with some vital
current challenges and future trends in SISR leveraging deep learning
algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM
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
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