Image Denoising via Sequential Ensemble Learning

Yang, Xuhui; Xu, Yong; Quan, Yuhui; Gao, Hui

doi:10.1109/tip.2020.2978645

Cited by 40 publications

(6 citation statements)

References 48 publications

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“…The denoising performance depends, therefore, on how well the training data matches the input data in terms of noise characteristics and content. Other supervised methods use, e.g., network ensambles [Yang et al, 2020] or a complex-valued convolutional neural network [Quan et al, 2021].…”

Section: Denoisingmentioning

confidence: 99%

Real-Time Multi-Material Reflectance Reconstruction for Large-Scale Scenes Under Uncontrolled Illumination from RGB-D Image Sequences

Bode

Merzbach

Stotko

et al. 2019

2019 International Conference on 3D Vision (3DV)

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You made my studies a genuinely memorable experience. I am also very thankful for my internships at Meta Reality Labs Research in 2020 and 2021. Special thanks go to Michael Goesele, Yujia Chen, Christophe Hery, and Carlos Aliaga for giving me these amazing opportunities and supervising me during the internships.For proofreading this thesis, I want to thank Sebastian Kappes, Ralf Riesen, and Patrick Stotko.Finally, I thank all of my friends and family for their tremendous support throughout my studies. It was a big help to know that I can always rely on you.

show abstract

Section: Denoisingmentioning

confidence: 99%

Real-Time Multi-Material Reflectance Reconstruction for Large-Scale Scenes Under Uncontrolled Illumination from RGB-D Image Sequences

Bode

Merzbach

Stotko

et al. 2019

2019 International Conference on 3D Vision (3DV)

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“…Ensemble learning is a machine learning algorithm, which is widely used in intrusion detection [1], network security [2], emotion recognition [3], image denoising [4], and so on. The ensemble classifier establishes multiple training models on the data set and constructs multiple independent or related base classifiers.…”

Section: Introductionmentioning

confidence: 99%

BGRF: A broad granular random forest algorithm

Chen

Yan

et al. 2023

IFS

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The random forest is a combined classification method belonging to ensemble learning. The random forest is also an important machine learning algorithm. The random forest is universally applicable to most data sets. However, the random forest is difficult to deal with uncertain data, resulting in poor classification results. To overcome these shortcomings, a broad granular random forest algorithm is proposed by studying the theory of granular computing and the idea of breadth. First, we granulate the breadth of the relationship between the features of the data sets samples and then form a broad granular vector. In addition, the operation rules of the granular vector are defined, and the granular decision tree model is proposed. Finally, the multiple granular decision tree voting method is adopted to obtain the result of the granular random forest. Some experiments are carried out on several UCI data sets, and the results show that the classification performance of the broad granular random forest algorithm is better than that of the traditional random forest algorithm.

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“…Ensemble learning uses a set of learners and applies rules to integrate the learning results, to obtain better performance than a single learner [16] . The effectiveness of ensemble learning has been widely demonstrated in a variety of applications [17][18][19] . Bae et al [20] proposed a modified m-CNN model that integrated images and text in multi-view learning for flower classification.…”

Section: Introduction mentioning

confidence: 99%

Dynamic ensemble selection of convolutional neural networks and its application in flower classification

Wang

et al. 2022

International Journal of Agricultural and Biological Engineering

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In recent years, convolutional neural networks (CNNs) have achieved great success in image classification. However, CNN models usually have complex network structures that tend to cause some related problems, such as redundancy of network parameters, low training efficiency, overfitting, and weak generalization ability. To solve these problems and improve the accuracy of flower classification, the advantages of CNNs were combined with those of ensemble learning and a method was developed for the dynamic ensemble selection of CNNs. First, MobileNet models pre-trained on a public dataset were transferred to flower datasets to train thirteen different MobileNet classifiers, and a resampling strategy was used to enhance the diversity of individual models. Second, the thirteen classifiers were sorted by a classifier sorting algorithm, before ensemble selection, to avoid an exhaustive search. Finally, with the credibility of recognition results, a classifier subset was dynamically selected and integrated to identify the flower species from their images. To verify the effectiveness, the proposed method was used to classify the images of five flower species. The accuracy of the proposed method was 95.50%, an improvement of 1.62%, 3.94%, 22.04%, 13.77%, and 0.44%, over those of MobileNet, Inception-v1, ResNet-50, Inception-ResNet-v2, and the linear ensemble method, respectively. In addition, the performance of the proposed method was compared with five other methods for flower classification. The experimental results demonstrated the accuracy and robustness of the proposed method.

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Image Denoising via Sequential Ensemble Learning

Cited by 40 publications

References 48 publications

Real-Time Multi-Material Reflectance Reconstruction for Large-Scale Scenes Under Uncontrolled Illumination from RGB-D Image Sequences

Real-Time Multi-Material Reflectance Reconstruction for Large-Scale Scenes Under Uncontrolled Illumination from RGB-D Image Sequences

BGRF: A broad granular random forest algorithm

Dynamic ensemble selection of convolutional neural networks and its application in flower classification

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