scispace - formally typeset
Search or ask a question
Topic

Upsampling

About: Upsampling is a research topic. Over the lifetime, 2426 publications have been published within this topic receiving 57613 citations.


Papers
More filters
Patent
17 Nov 1999
TL;DR: In this article, the input signal is divided into a plurality of subbands with the aid of bank of complex valued, single-sided subband filters, which make aliasing negligible at near twice the critical downsampling rates.
Abstract: In a method of processing an input signal, the input signal is divided into a plurality of subbands with the aid of bank of complex valued, single-sided subband filters. The single-sided frequency spectra of the resulting subbands make aliasing negligible at near twice the critical downsampling rates.

9 citations

Journal ArticleDOI
TL;DR: The results demonstrate that multirate ILC schemes are able to achieve not only monotonic learning transient, but also much better tracking accuracy than conventional one-step-ahead I LC schemes.

9 citations

Journal ArticleDOI
TL;DR: A real-time image super-resolution method with good reconstruction performance that replaces the default upsampling method (bicubic interpolation) with a pixel shuffling layer and is not only fast but also accurate.
Abstract: The aim of single-image super-resolution is to recover a high-resolution image based on a low-resolution image. Deep convolutional neural networks have largely enhanced the reconstruction performance of image super-resolution. Since the input image is always bicubic-interpolated, the main weakness of deep convolutional neural networks is that they are time-consuming. Moreover, fast convolutional neural networks can perform real-time image super-resolution but are unable to achieve reliable performance. To address those drawbacks, we propose a real-time image super-resolution method with good reconstruction performance. We replace the default upsampling method (bicubic interpolation) with a pixel shuffling layer. Local and global residual connections are taken to guarantee better performance. As shown in Fig. 1, our proposed method is not only fast but also accurate.

9 citations

Posted Content
TL;DR: This work introduces an end-to-end fully-recurrent hourglass-shaped neural network architecture with residual connections for waveform-based single-channel speech enhancement that outperforms state-of-the-art approaches in six evaluation metrics.
Abstract: Most current speech enhancement models use spectrogram features that require an expensive transformation and result in phase information loss. Previous work has overcome these issues by using convolutional networks to learn long-range temporal correlations across high-resolution waveforms. These models, however, are limited by memory-intensive dilated convolution and aliasing artifacts from upsampling. We introduce an end-to-end fully-recurrent hourglass-shaped neural network architecture with residual connections for waveform-based single-channel speech enhancement. Our model can efficiently capture long-range temporal dependencies by reducing the features resolution without information loss. Experimental results show that our model outperforms state-of-the-art approaches in six evaluation metrics.

9 citations

Posted Content
Hongying Liu1, Peng Zhao1, Zhubo Ruan1, Fanhua Shang1, Yuanyuan Liu1 
TL;DR: Wang et al. as discussed by the authors designed a U-shaped residual dense network with 3D convolution (U3D-RDN) for fine implicit motion estimation and motion compensation (MEMC) as well as coarse spatial feature extraction.
Abstract: Video super-resolution (VSR) aims at restoring a video in low-resolution (LR) and improving it to higher-resolution (HR). Due to the characteristics of video tasks, it is very important that motion information among frames should be well concerned, summarized and utilized for guidance in a VSR algorithm. Especially, when a video contains large motion, conventional methods easily bring incoherent results or artifacts. In this paper, we propose a novel deep neural network with Dual Subnet and Multi-stage Communicated Upsampling (DSMC) for super-resolution of videos with large motion. We design a new module named U-shaped residual dense network with 3D convolution (U3D-RDN) for fine implicit motion estimation and motion compensation (MEMC) as well as coarse spatial feature extraction. And we present a new Multi-Stage Communicated Upsampling (MSCU) module to make full use of the intermediate results of upsampling for guiding the VSR. Moreover, a novel dual subnet is devised to aid the training of our DSMC, whose dual loss helps to reduce the solution space as well as enhance the generalization ability. Our experimental results confirm that our method achieves superior performance on videos with large motion compared to state-of-the-art methods.

9 citations


Network Information
Related Topics (5)
Convolutional neural network
74.7K papers, 2M citations
90% related
Image segmentation
79.6K papers, 1.8M citations
90% related
Feature extraction
111.8K papers, 2.1M citations
89% related
Deep learning
79.8K papers, 2.1M citations
88% related
Feature (computer vision)
128.2K papers, 1.7M citations
87% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023469
2022859
2021330
2020322
2019298
2018236