Gradient boosting for single image super-resolution

Xiong, Dongping; Gui, Qiuling; Hou, Wenguang; Ding, Mingyue

doi:10.1016/j.ins.2018.04.075

Cited by 15 publications

(4 citation statements)

References 27 publications

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“…To exploit the gradient information to assist the image reconstruction, Xiong et al. [41] proposed a SISR method based on the gradient boosting framework. Then Ma et al.…”

Section: Related Workmentioning

confidence: 99%

“…Zhang et al [40] further revised RDN and applied it to image restoration, which made a remarkable performance improvement. To exploit the gradient information to assist the image reconstruction, Xiong et al [41] proposed a SISR method based on the gradient boosting framework. Then Ma et al [42] proposed SPSR by exploiting gradient information to guide the SR reconstruction.…”

Section: Introductionmentioning

confidence: 99%

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Fused pyramid attention network for single image super‐resolution

Chen

Zhang

2023

IET Image Processing

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In image super‐resolution, deep neural networks with various attention mechanisms have achieved noticeable performance in recent years, for example, channel attention and layer attention. Although many researchers have achieved good super‐resolution results with only a certain style of attention, the divergence and the complementarity focused by multiple attention mechanisms are ignored. In addition, most of these methods fail to utilize the diverse information from multi‐scale features. To efficiently manipulate the above rich information, this paper strives to combine multi‐scale structure and multi‐attention schemes in architecture and module levels for super‐resolution. Especially, in the architecture level, a fused pyramid attention network is developed to extract deep features with the multi‐scale context information from multiple different sizes of receptive field recurrently with skip connections. For the module level, a fused pyramid attention module is designed to fuse the two attention mechanisms to further refine the deep features with fine‐grained information. Compared with the common fusion strategy, the adopted feature fusion structure can maintain better structural information while establishing long‐range dependency. Extensive experimental results demonstrate that the proposed network achieves favorable performance quantitatively and visually.

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“…To exploit the gradient information to assist the image reconstruction, Xiong et al. [41] proposed a SISR method based on the gradient boosting framework. Then Ma et al.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fused pyramid attention network for single image super‐resolution

Chen

Zhang

2023

IET Image Processing

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show abstract

“…A+ [21], an improved version of ANR, learns regressors on all training patches. There also exist SR methods based on decision trees or random forests such as [22]- [26] to address the SISR problem.…”

Section: Related Workmentioning

confidence: 99%

Multi-Scale Residual Hierarchical Dense Networks for Single Image Super-Resolution

et al. 2019

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Single image super-resolution is known to be an ill-posed problem, which has been studied for decades. With the developments of deep convolutional neural networks, the CNN-based single image super-resolution methods have greatly improved the quality of the generated high-resolution images. However, it is difficult for image super-resolution to make full use of the relationship between pixels in low-resolution images. To address this issue, we propose a novel multi-scale residual hierarchical dense network, which tries to find the dependencies in multi-level and multi-scale features. Especially, we apply the atrous spatial pyramid pooling, which concatenates multiple atrous convolutions with different dilation rates, and design a residual hierarchical dense structure for single image super-resolution. The atrous-spatialpyramid-pooling module is used for learning the relationship of features at multiple scales while the residual hierarchical dense structure, which consists of several hierarchical dense blocks with skip connections, aims to adaptively detect key information from multi-level features. Meanwhile, dense features from different groups are connected in a dense approach by hierarchical dense blocks, which can adequately extract local multi-level features. The extensive experiments on benchmark datasets illustrate the superiority of our proposed method compared with the state-of-the-art methods. The super-resolution results on benchmark datasets of our method can be downloaded from https://github.com/Rainyfish/MS-RHDN, and the source code will be released upon acceptance of the paper.INDEX TERMS Convolutional neural networks, deep learning, multi-scale residual hierarchical dense, image super-resolution.

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“…These LR-HR patch pairs are cropped from a database composed of LR-HR image pairs. Many learning algorithms have been proposed to learn the mapping models, including dictionary learning [17, 18, 22, 40, 41, 46, 56, 58-60, 65, 66, 70, 76], regression [11,47,48,58,59,64], decision tree [24,62], random forest [23,25,53] and convolutional neural network (CNN) [13,14,33,34,36,37,52,57]. Linear regression models [31] have higher prediction speed than non-linear regressions models.…”

Section: Introductionmentioning

confidence: 99%

Piecewise linear regression-based single image super-resolution via Hadamard transform

Sun

Yiu

Jin

et al. 2018

Information Sciences

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Image super-resolution (SR) has extensive applications in surveillance systems, satellite imaging, medical imaging, and ultra-high definition display devices. The state-ofthe-art methods for SR still incur considerable running time. In this paper, we propose a novel approach based on Hadamard pattern and tree search structure in order to reduce the running time significantly. In this approach, LR (low-resolution)-HR (high-resolution) training patch pairs are classified into different classes based on the Hadamard patterns generated from the LR training patches. The mapping relationship between the LR space and the HR space for each class is then learned and used for SR. Experimental results show that the proposed method can achieve comparable accuracy as state-of-the-art methods with much faster running speed. The dataset, pretrained models and source code can be accessed at this URL † .

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Gradient boosting for single image super-resolution

Cited by 15 publications

References 27 publications

Fused pyramid attention network for single image super‐resolution

Fused pyramid attention network for single image super‐resolution

Multi-Scale Residual Hierarchical Dense Networks for Single Image Super-Resolution

Piecewise linear regression-based single image super-resolution via Hadamard transform

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