Inception learning super-resolution

Haris, Muhammad; Widyanto, M. Rahmat; Nobuhara, Hajime

doi:10.1364/ao.56.006043

Cited by 18 publications

(12 citation statements)

References 6 publications

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“…S IGNIFICANT progress in deep learning for vision [1], [2], [3], [4], [5], [6], [7] has recently been propagating to the field of super-resolution (SR) [8], [9], [10], [11], [12], [13], [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Deep Back-Projection Networks for Super-Resolution

Haris

Shakhnarovich

Ukita

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

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1,322

940

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Previous feed-forward architectures of recently proposed deep super-resolution networks learn the features of low-resolution inputs and the non-linear mapping from those to a high-resolution output. However, this approach does not fully address the mutual dependencies of low-and high-resolution images. We propose Deep Back-Projection Networks (DBPN), the winner of two image super-resolution challenges (NTIRE2018 and PIRM2018), that exploit iterative up-and down-sampling layers. These layers are formed as a unit providing an error feedback mechanism for projection errors. We construct mutually-connected up-and down-sampling units each of which represents different types of image degradation and high-resolution components. We also show that extending this idea to several variants applying the latest deep network trends, such as recurrent network, dense connection, and residual learning, to improve the performance. The experimental results yield superior results and in particular establishing new state-of-the-art results across multiple data sets, especially for large scaling factors such as 8×.

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Section: Introductionmentioning

confidence: 99%

Deep Back-Projection Networks for Super-Resolution

Haris

Shakhnarovich

Ukita

2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Self Cite

1,322

940

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“…The goal of super-resolution (SR) is to enhance a lowresolution (LR) image to higher resolution (HR) by filling missing fine details in the LR image. This field can be divided into Single-Image SR (SISR) [4,8,9,19,21,29], Multi-Image SR (MISR) [5,6], and Video SR (VSR) [2,30,27,16,13,25], the focus of this paper.…”

Section: Introductionmentioning

confidence: 99%

Recurrent Back-Projection Network for Video Super-Resolution

Haris¹,

Shakhnarovich

Ukita³

2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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458

356

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We proposed a novel architecture for the problem of video super-resolution. We integrate spatial and temporal contexts from continuous video frames using a recurrent encoder-decoder module, that fuses multi-frame information with the more traditional, single frame super-resolution path for the target frame. In contrast to most prior work where frames are pooled together by stacking or warping, our model, the Recurrent Back-Projection Network (RBPN) treats each context frame as a separate source of information. These sources are combined in an iterative refinement framework inspired by the idea of back-projection in multiple-image super-resolution. This is aided by explicitly representing estimated inter-frame motion with respect to the target, rather than explicitly aligning frames. We propose a new video super-resolution benchmark, allowing evaluation at a larger scale and considering videos in different motion regimes. Experimental results demonstrate that our RBPN is superior to existing methods on several datasets.

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“…In addition, improving the network structure can improve the learning ability and reliable ability of the neural network. For example, using the Inception of modules and convolution in GoogLeNet can help neural network in different areas to capture more target-oriented characteristic, accelerate the calculation speed, and increase the depth of the neural network [34,35]. Besides, Xception module is the extreme version of Inception [36]; Xception module completely decoupled across the channel correlation and spatial correlation and has achieved the classification accuracy of 94.5% in the classification of ImageNet database [37].…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Wang

Zheng

et al. 2019

Complexity

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In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module (XCN) is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time-frequency graphs are obtained by wavelet time-frequency analysis. Then the time-frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time-frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.

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Inception learning super-resolution

Cited by 18 publications

References 6 publications

Deep Back-Projection Networks for Super-Resolution

Deep Back-Projection Networks for Super-Resolution

Recurrent Back-Projection Network for Video Super-Resolution

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

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