Deep coordinate attention network for single image super‐resolution

Xie, Chao; Zhu, Hongyu; Fei, Yeqi

doi:10.1049/ipr2.12364

Cited by 38 publications

(18 citation statements)

References 47 publications

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“…erefore, on the basis of analyzing the modeling characteristics and problems of traditional forecasting methods, this paper introduces the cultural space layout planning forecasting method based on the forward three-layer neural network model and analyzes the neural network for establishing time series and regression analysis of two types of data. Taking the time series type as an example, the detailed technical design of the cultural space demand forecasting model based on the forward three-layer neural network is carried out, including network model selection, network structure design, sample construction, parameter setting, select the region and historical total population forecast as verification examples, establish a linear neural network model of population forecast for simulation forecasting, to verify the forecasting ability of the forward three-layer neural network model, and compare and analyze the artificial neural network Similarities and differences between network prediction models and traditional prediction methods [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Layout Optimization of Urban Cultural Space Construction Based on Forward Three-Layer Neural Network Model

Zhou

2022

Computational Intelligence and Neuroscience

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The change of urban cultural space layout is a multi-variable, multi-objective, and restricted research process. The optimization of urban cultural space construction and layout is a multi-objective decision-making problem that needs to be solved urgently. Based on the forward three-layer neural network theory, this paper constructs an optimization model for the construction and layout of urban cultural space evaluation of the layout of cultural space. This paper first analyzes the feasibility of combining the forward three-layer neural network model with the optimization and adjustment of cultural space layout structure. Taking the three-layer feedforward network as an example, the structure optimization model based on the forward three-layer neural network is selected, and the established model is used to reflect the internal environment of the objective world. Structure and perform dynamic simulation. In the process of simulation modeling, from the aspects of system description, model structure, logical analysis, reasoning, and interpretation, two effective computer dynamic simulation methods, namely, forward three-layer neural network model and system dynamics SD model, were carried out for theoretical comparison and identification. The experimental results show that the feasibility and calculation error of the application of the optimization model are relatively good, reaching 0.897 and 6.21%, respectively. The number of newly added cultural spaces and the expansion speed show an increasing trend, expanding at an average annual speed of about 35 km2, effectively increasing the quality of regional planning and construction layout.

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Section: Introductionmentioning

confidence: 99%

Layout Optimization of Urban Cultural Space Construction Based on Forward Three-Layer Neural Network Model

Zhou

2022

Computational Intelligence and Neuroscience

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“…Application in real-world cases . To further test the performance of our method in real-world scenes, we captured three remote-sensing images from the Landsat-8 satellite [ 64 , 65 , 66 , 67 ], which are the landscapes around Xuanwu Lake, Xinjizhou National Wetland Park, and Lukou International Airport in Nanjing. The original size of these images is 900 × 619.…”

Section: Remote Sensing Image Super-resolutionmentioning

confidence: 99%

Lightweight Single Image Super-Resolution with Selective Channel Processing Network

Zhu

Tang

et al. 2022

Sensors

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With the development of deep learning, considerable progress has been made in image restoration. Notably, many state-of-the-art single image super-resolution (SR) methods have been proposed. However, most of them contain many parameters, which leads to a significant amount of calculation consumption in the inference phase. To make current SR networks more lightweight and resource-friendly, we present a convolution neural network with the proposed selective channel processing strategy (SCPN). Specifically, the selective channel processing module (SCPM) is first designed to dynamically learn the significance of each channel in the feature map using a channel selection matrix in the training phase. Correspondingly, in the inference phase, only the essential channels indicated by the channel selection matrixes need to be further processed. By doing so, we can significantly reduce the parameters and the calculation consumption. Moreover, the differential channel attention (DCA) block is proposed, which takes into consideration the data distribution of the channels in feature maps to restore more high-frequency information. Extensive experiments are performed on the natural image super-resolution benchmarks (i.e., Set5, Set14, B100, Urban100, Manga109) and remote-sensing benchmarks (i.e., UCTest and RESISCTest), and our method achieves superior results to other state-of-the-art methods. Furthermore, our method keeps a slim size with fewer than 1 M parameters, which proves the superiority of our method. Owing to the proposed SCPM and DCA, our SCPN model achieves a better trade-off between calculation cost and performance in both general and remote-sensing SR applications, and our proposed method can be extended to other computer vision tasks for further research.

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“…But SE only encodes the information between channels, ignoring the equally important spatial relationship, which is used to capture objects in vision tasks. Spatial information is also crucial in capturing target structures in visual tasks [23]. Later, Woo et al proposed a Convolutional Block Attention Module (CBAM) [24].…”

Section: B Coordinate Attention Modulmentioning

confidence: 99%

Detection Method of Damaged Camellia Oleifera Seeds Based on YOLOv5-CB

et al. 2022

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To solve the problems that the existing sorting equipment cannot effectively identify and sort damaged Camellia oleifera seeds and traditional manual sorting of damaged Camellia oleifera seeds is inefficient and slow, in this paper, a damaged Camellia oleifera seeds detection method based on YOLOv5, coordinate attention, and weighted bidirectional feature pyramid network was designed. In this study, according to the actual requirements, firstly, the Coordinate Attention module (CA) was added to the YOLOv5 algorithm to improve the detection precision of damaged Camellia oleifera seeds in stacked Camellia oleifera seeds. Secondly, the network structure was optimized and the weighted bi-directional feature pyramid network (BiFPN) was added. The module integrates multi-scale features from top to bottom to reduce the missed detection of slightly damaged Camellia seeds. The final experimental results show that compared with the original YOLOv5 model, the detection precision of the improved model YOLOV5-CB is improved by 6.1%, reaching 92.4%, and the mean Average Precision (mAP) is also improved from 87.7% to 93.4%, the average detection time of a single Camellia seeds image is 6.4ms, which meet the requirements of precision and real-time in practical application.

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Deep coordinate attention network for single image super‐resolution

Cited by 38 publications

References 47 publications

Layout Optimization of Urban Cultural Space Construction Based on Forward Three-Layer Neural Network Model

Layout Optimization of Urban Cultural Space Construction Based on Forward Three-Layer Neural Network Model

Lightweight Single Image Super-Resolution with Selective Channel Processing Network

Detection Method of Damaged Camellia Oleifera Seeds Based on YOLOv5-CB

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