Self-attention negative feedback network for real-time image super-resolution

Liu, Xiangbin; Chen, Shuqi; Song, Liping; Woźniak, Marcin; Liu, Shuai

doi:10.1016/j.jksuci.2021.07.014

Cited by 49 publications

(20 citation statements)

References 16 publications

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“… Liu et al (2021) proposes a self-attention negative feed-back network (SRAFBN) for realizing the real-time image super-resolution (SR). The network model constrains the image mapping space and selects the key information of the image through the self-attention negative feedback model, so that higher quality images can be generated to meet human visual perception.…”

Section: Resultsmentioning

confidence: 99%

Automatic and Accurate Calculation of Rice Seed Setting Rate Based on Image Segmentation and Deep Learning

Guo

Zhang

et al. 2021

Front. Plant Sci.

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The rice seed setting rate (RSSR) is an important component in calculating rice yields and a key phenotype for its genetic analysis. Automatic calculations of RSSR through computer vision technology have great significance for rice yield predictions. The basic premise for calculating RSSR is having an accurate and high throughput identification of rice grains. In this study, we propose a method based on image segmentation and deep learning to automatically identify rice grains and calculate RSSR. By collecting information on the rice panicle, our proposed image automatic segmentation method can detect the full grain and empty grain, after which the RSSR can be calculated by our proposed rice seed setting rate optimization algorithm (RSSROA). Finally, the proposed method was used to predict the RSSR during which process, the average identification accuracy reached 99.43%. This method has therefore been proven as an effective, non-invasive method for high throughput identification and calculation of RSSR. It is also applicable to soybean yields, as well as wheat and other crops with similar characteristics.

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Section: Resultsmentioning

confidence: 99%

Automatic and Accurate Calculation of Rice Seed Setting Rate Based on Image Segmentation and Deep Learning

Guo

Zhang

et al. 2021

Front. Plant Sci.

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“…In future work, the authors plan to expand the number of samples in the dataset and, if possible, to collaborate with other centres with experience in kidney pathology for further completion and external multicentre validation. In the meantime, the authors will investigate more advanced AI models such as the correlation learning mechanism for deep neural networks [38] and real-time image super-resolution reconstruction [39] to improve performance. Additionally, developing a deep learning approach to evaluate other histological stains such as immunofluorescence images, as performed by Giulia et al [40], is also feasible.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Artificial Intelligence System for Automatic Assessment of Glomerular Pathological Findings in Lupus Nephritis

et al. 2021

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Accurate assessment of renal histopathology is crucial for the clinical management of patients with lupus nephritis (LN). However, the current classification system has poor interpathologist agreement. This paper proposes a deep convolutional neural network (CNN)-based system that detects and classifies glomerular pathological findings in LN. A dataset of 349 renal biopsy whole-slide images (WSIs) (163 patients with LN, periodic acid-Schiff stain, 3906 glomeruli) annotated by three expert nephropathologists was used. The CNN models YOLOv4 and VGG16 were employed to localise the glomeruli and classify glomerular lesions (slight/severe impairments or sclerotic lesions). An additional 321 unannotated WSIs from 161 patients were used for performance evaluation at the per-patient kidney level. The proposed model achieved an accuracy of 0.951 and Cohen’s kappa of 0.932 (95% CI 0.915–0.949) for the entire test set for classifying the glomerular lesions. For multiclass detection at the glomerular level, the mean average precision of the CNN was 0.807, with ‘slight’ and ‘severe’ glomerular lesions being easily identified (F1: 0.924 and 0.952, respectively). At the per-patient kidney level, the model achieved a high agreement with nephropathologist (linear weighted kappa: 0.855, 95% CI: 0.795–0.916, p < 0.001; quadratic weighted kappa: 0.906, 95% CI: 0.873–0.938, p < 0.001). The results suggest that deep learning is a feasible assistive tool for the objective and automatic assessment of pathological LN lesions.

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“…Most of the previous works are not designed for the UAVs nor optimized for detection and localization tasks during flying on the air. It is important to realize the real-time understanding of frames’ contextual information [ 20 , 21 ].…”

Section: Related Workmentioning

confidence: 99%

A Path Planning Method with Perception Optimization Based on Sky Scanning for UAVs

Yuan

Ota

Dong

et al. 2022

Sensors

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Unmanned aerial vehicles (UAVs) are frequently adopted in disaster management. The vision they provide is extremely valuable for rescuers. However, they face severe problems in their stability in actual disaster scenarios, as the images captured by the on-board sensors cannot consistently give enough information for deep learning models to make accurate decisions. In many cases, UAVs have to capture multiple images from different views to output final recognition results. In this paper, we desire to formulate the fly path task for UAVs, considering the actual perception needs. A convolutional neural networks (CNNs) model is proposed to detect and localize the objects, such as the buildings, as well as an optimization method to find the optimal flying path to accurately recognize as many objects as possible with a minimum time cost. The simulation results demonstrate that the proposed method is effective and efficient, and can address the actual scene understanding and path planning problems for UAVs in the real world well.

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Self-attention negative feedback network for real-time image super-resolution

Cited by 49 publications

References 16 publications

Automatic and Accurate Calculation of Rice Seed Setting Rate Based on Image Segmentation and Deep Learning

Automatic and Accurate Calculation of Rice Seed Setting Rate Based on Image Segmentation and Deep Learning

Deep Learning-Based Artificial Intelligence System for Automatic Assessment of Glomerular Pathological Findings in Lupus Nephritis

A Path Planning Method with Perception Optimization Based on Sky Scanning for UAVs

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