Mixed distortion image enhancement method based on joint of deep residuals learning and reinforcement learning

Wang, Xiaohong; Liu, Fang; Ma, Xiangcai

doi:10.1007/s11760-020-01824-y

Cited by 7 publications

(3 citation statements)

References 20 publications

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“…After the stack is completed, two depthwise separable convolutions are performed to obtain a final effective feature layer, which is the feature concentration of the entire picture. Finally, the model uses a 1 × 1 convolution to adjust the final effective feature layer and uses the resize function to upsample so that the width and height of the final output layer are consistent with the input image [27,28]. The overall architecture of the DeepLabv3+ model is shown in Figure 15.…”

Section: The Image Semantic Segmentation Experiments Of the Coal-rock...mentioning

confidence: 99%

Research on Coal and Rock Recognition in Coal Mining Based on Artificial Neural Network Models

Sui,

Zhang,

Sun

et al. 2024

Applied Sciences

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In the process of coal mining, one of the main reasons for the high labor intensity of workers and the frequent occurrence of casualties is the low level of intelligence of coal mining equipment. As the core equipment in the process of coal mining, the intelligence level of shearers directly affects the safety production and mining efficiency of coal mines. Coal and rock recognition technology is the core technology used to realize the intelligentization of shearers, which is an urgent technical problem to be solved in the field of coal mining. In this paper, coal seam images, rock stratum images, and coal–rock mixed-layer images of a coal mining area are taken as the research object, and key technologies such as the construction of a sample image library, classification and recognition, and semantic segmentation are studied by using the relevant theoretical knowledge of artificial neural network models. Firstly, the BP neural network is used to classify and identify coal seam images, rock stratum images, and coal–rock mixed-layer images, so as to distinguish which of the current mining targets of a shearer is the coal seam, rock stratum, or coal–rock mixed layer. Because different mining objectives will lead to different working modes of a shearer, it is necessary to maintain normal power to cut coal when encountering a coal seam, to stop working when encountering rock stratum, and to cut coal along the boundary between a coal seam and rock stratum when encountering a coal–rock mixed stratum. Secondly, the DeepLabv3+ model is used to perform semantic segmentation experiments on the coal–rock mixed-layer images. The purpose is to find out the distribution of coal and rocks in the coal–rock mixed layer in the coal mining area, so as to provide technical support for the automatic adjustment height of the shearer. Finally, the research in this paper achieved a 97.16% recognition rate in the classification and recognition experiment of the coal seam images, rock stratum images, and coal–rock mixed-layer images and a 91.2% accuracy in the semantic segmentation experiment of the coal–rock mixed-layer images. The research results of the two experiments provide key technical support for improving the intelligence level of shearers.

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Section: The Image Semantic Segmentation Experiments Of the Coal-rock...mentioning

confidence: 99%

Research on Coal and Rock Recognition in Coal Mining Based on Artificial Neural Network Models

Sui,

Zhang,

Sun

et al. 2024

Applied Sciences

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“…RL research has been developed in several topics, including robotics [113][114][115], design automation [25], energy management strategies for hybrid vehicles [43], parameter estimation in the context of biological systems [44,116,117], in facial motion learning [48,50,118], and have also been successfully applied in closed-world environments, such as games [51,54,119,120]. In the topic of image processing, some pertinent studies were found, especially using DRL [31,47,57,121]. Many novel applications continue to be proposed by researchers.…”

Section: Research Using Reinforcement Learningmentioning

confidence: 99%

Developments in Image Processing Using Deep Learning and Reinforcement Learning

Valente,

António,

Mora

et al. 2023

J. Imaging

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The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for society in general. In addition to being larger, datasets are increasingly complex, bringing new theoretical and computational challenges. Alongside this evolution, data science tools have exploded in popularity over the past two decades due to their myriad of applications when dealing with complex data, their high accuracy, flexible customization, and excellent adaptability. When it comes to images, data analysis presents additional challenges because as the quality of an image increases, which is desirable, so does the volume of data to be processed. Although classic machine learning (ML) techniques are still widely used in different research fields and industries, there has been great interest from the scientific community in the development of new artificial intelligence (AI) techniques. The resurgence of neural networks has boosted remarkable advances in areas such as the understanding and processing of images. In this study, we conducted a comprehensive survey regarding advances in AI design and the optimization solutions proposed to deal with image processing challenges. Despite the good results that have been achieved, there are still many challenges to face in this field of study. In this work, we discuss the main and more recent improvements, applications, and developments when targeting image processing applications, and we propose future research directions in this field of constant and fast evolution.

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“…Deep Learning has been proven to be an excellent choice for solving classification tasks. It has been wildly used in various areas, such as Reinforcement Learning (RL) and Computer Vision [ 21 , 22 ]. In a variety of problems, the required features to classify samples are embedded in a single image.…”

Section: Introductionmentioning

confidence: 99%

A hand rubbing classification model based on image sequence enhanced by feature-based confidence metric

Haghpanah

Masouleh

Kalhor

et al. 2023

SIViP

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Hand hygiene is critical for declining the spread of viruses and diseases. Over recent years, it has been globally known as one of the most effective ways against COVID-19 outbreak. The World Health Organization (WHO) has suggested a 12-step guideline for hand rubbing. Due to the importance of this guideline, several studies have been conducted to measure compliance with it using Computer Vision. However, almost all of them are based on processing single images as input, referred to as baseline models in this paper. This study proposes a sequence model in order to process sequences of consecutive images as input. The model is a mixture of Inception-ResNet architecture for spatial feature extraction and LSTM for detecting time-series information. After training the model on a comprehensive dataset, an accuracy of 98.99% was achieved on the test set. Compared to the best baseline models, the proposed sequence model is correspondingly about 1% and 4% better in terms of accuracy and confidence, though 3 times slower in inference time. Furthermore, this study demonstrates that the accuracy metric is not necessarily adequate to compare different models and optimize their hyperparameters. Accordingly, the Feature-Based Confidence Metric was utilized in order to provide a more pleasing comparison to discriminate the proposed sequence model with the best baseline model and optimize its hyperparameters.

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Mixed distortion image enhancement method based on joint of deep residuals learning and reinforcement learning

Cited by 7 publications

References 20 publications

Research on Coal and Rock Recognition in Coal Mining Based on Artificial Neural Network Models

Research on Coal and Rock Recognition in Coal Mining Based on Artificial Neural Network Models

Developments in Image Processing Using Deep Learning and Reinforcement Learning

A hand rubbing classification model based on image sequence enhanced by feature-based confidence metric

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