AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results

Zhang, Kai; Nan, Nan; Li, Chenghua; Zou, Xueyi; Kang, Ning; Wang, Zhan; Xu, Hang; Wang, Chaofeng; Li, Zheng; Wang, Linlin; Shi, Jun; Gu, Shuhang; Sun, Wei; Lang, Zhiqiang; Nie, Jiangtao; Wei, Wei; Zhang, Lei; Niu, Yazhe; Zhuo, Peijin; Kong, Xiangzhen; Sun, Lei; Wang, Wenhao; Timofte, Radu; Hui, Zheng; Wang, Xiumei; Gao, Xinbo; Xiong, Dongliang; Liu, Shuai; Gang, Ruipeng

doi:10.1109/iccvw.2019.00441

Cited by 48 publications

(23 citation statements)

References 76 publications

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“…Following [62], we propose a reconstruction module with information multi-distillation blocks (IMDB). By adopting distillation mechanism to gradually extract and process hierarchical features, superior SR performance can be achieved with a small number of parameters and a low computational cost [63].…”

Section: Reconstruction and Upsampling Modulementioning

confidence: 99%

Light Field Image Super-Resolution Using Deformable Convolution

Wang

Yang

Wang

et al. 2021

IEEE Trans. on Image Process.

100

116

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Section: Reconstruction and Upsampling Modulementioning

confidence: 99%

Light Field Image Super-Resolution Using Deformable Convolution

Wang

Yang

Wang

et al. 2021

IEEE Trans. on Image Process.

100

116

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“…Liu et al [ 44 ] recently proposed a lightweight residual feature distillation network using a shallow residual block and multiple feature distillation connections to learn more discriminative representations. The proposed model was the winning solution for the advances in image manipulation 2020 (AIM2020) constrained image super-resolution challenge [ 45 ].…”

Section: Related Workmentioning

confidence: 99%

Monocular Depth Estimation with Joint Attention Feature Distillation and Wavelet-Based Loss Function

Liu

Zhang

Meng

et al. 2020

Sensors

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Depth estimation is a crucial component in many 3D vision applications. Monocular depth estimation is gaining increasing interest due to flexible use and extremely low system requirements, but inherently ill-posed and ambiguous characteristics still cause unsatisfactory estimation results. This paper proposes a new deep convolutional neural network for monocular depth estimation. The network applies joint attention feature distillation and wavelet-based loss function to recover the depth information of a scene. Two improvements were achieved, compared with previous methods. First, we combined feature distillation and joint attention mechanisms to boost feature modulation discrimination. The network extracts hierarchical features using a progressive feature distillation and refinement strategy and aggregates features using a joint attention operation. Second, we adopted a wavelet-based loss function for network training, which improves loss function effectiveness by obtaining more structural details. The experimental results on challenging indoor and outdoor benchmark datasets verified the proposed method’s superiority compared with current state-of-the-art methods.

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“…At each step, its partial feature information was retained and the other features were processed in a subsequent step. It won the first place in the AIM 2019 constrained image super-resolution challenge [23]. In addition to the above-mentioned strategies, some other efficient methods (e.g.…”

Section: Introductionmentioning

confidence: 99%

Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution

Wu,

Gui,

Zhang

et al. 2021

Preprint

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Recently, deep convolutional neural network methods have achieved an excellent performance in image superresolution (SR), but they can not be easily applied to embedded devices due to large memory cost. To solve this problem, we propose a pyramidal dense attention network (PDAN) for lightweight image super-resolution in this paper. In our method, the proposed pyramidal dense learning can gradually increase the width of the densely connected layer inside a pyramidal dense block to extract deep features efficiently. Meanwhile, the adaptive group convolution that the number of groups grows linearly with dense convolutional layers is introduced to relieve the parameter explosion. Besides, we also present a novel joint attention to capture cross-dimension interaction between the spatial dimensions and channel dimension in an efficient way for providing rich discriminative feature representations. Extensive experimental results show that our method achieves superior performance in comparison with the state-of-the-art lightweight SR methods.

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AIM 2019 Challenge on Constrained Super-Resolution: Methods and Results

Cited by 48 publications

References 76 publications

Light Field Image Super-Resolution Using Deformable Convolution

Light Field Image Super-Resolution Using Deformable Convolution

Monocular Depth Estimation with Joint Attention Feature Distillation and Wavelet-Based Loss Function

Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution

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