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Application of deep reinforcement learning in various image processing tasks: a survey

EVOLVING systems 2025年第1期16卷 1-20页

作者： Tadesse, Daniel Moges Kebede, Samuel Rahimeto Debele, Taye Girma Waldamichae, Fraol Gelana Ethiopian Artificial Intelligence Inst Addis Ababa 40782 Ethiopia Addis Ababa Sci & Technol Univ Coll Elect & Mech Engn Addis Ababa 120611 Ethiopia Debreberhan Univ Coll Engn Debreberhan 222 Ethiopia

A subset of machine learning algorithm called Deep Reinforcement learning (DRL) enables computers or agents to learn behavior by taking actions in a given environment through trial and error while observing the rewards. In this learning paradigm, the agent is given a set of actions to chose and is then rewarded or punished depending on the results of those actions. The agent gradually develops the ability to make the best decisions by maximizing its rewards. DRL blends the learning ability of deep neural networks into the decision making capability of reinforcement learning (RL) frameworks in order to seeks and identify the most favorable set of actions. This survey paper studies DRL applications for diverse image processing tasks. It starts by providing an overview of the latest model-free and model-based RL and DRL algorithms. Then, it looks at how DRL is being used for various image processing tasks including image segmentation and classification, object detection, image registration, image denoising, image restoration, and landmark detection. Lastly, the paper discusses the potential uses and challenges of DRL in the proposed area by addressing the research questions. Survey results have showed that DRL is a promising approach for image processing and that it has the potential to solve complex image processing tasks.

关键词： Deep reinforcement learning Deep neural networks image processing

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Code-Free machine learning Solutions for Microscopy image processing: Deep learning

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TISSUE ENGINEERING PART A 2024年第19-20期30卷 627-639页

作者： Chechekhina, Elizaveta Voloshin, Nikita Kulebyakin, Konstantin Tyurin-Kuzmin, Pyotr Lomonosov Moscow State Univ Fac Med Dept Biochem & Regenerat Biomed Moscow Russia Lomonosov Moscow State Univ Fac Med Dept Biochem & Regenerat Biomed Moscow 119991 Russia

In recent years, there has been a significant expansion in the realm of processing microscopy images, thanks to the advent of machine learning techniques. These techniques offer diverse applications for image processing. Currently, numerous methods are used for processing microscopy images in the field of biology, ranging from conventional machine learning algorithms to sophisticated deep learning artificial neural networks with millions of parameters. However, a comprehensive grasp of the intricacies of these methods usually necessitates proficiency in programming and advanced mathematics. In our comprehensive review, we explore various widely used deep learning approaches tailored for the processing of microscopy images. Our emphasis is on algorithms that have gained popularity in the field of biology and have been adapted to cater to users lacking programming expertise. In essence, our target audience comprises biologists interested in exploring the potential of deep learning algorithms, even without programming skills. Throughout the review, we elucidate each algorithm's fundamental concepts and capabilities without delving into mathematical and programming complexities. Crucially, all the highlighted algorithms are accessible on open platforms without requiring code, and we provide detailed descriptions and links within our review. It's essential to recognize that addressing each specific problem demands an individualized approach. Consequently, our focus is not on comparing algorithms but on delineating the problems they are adept at solving. In practical scenarios, researchers typically select multiple algorithms suited to their tasks and experimentally determine the most effective one. It is worth noting that microscopy extends beyond the realm of biology;its applications span diverse fields such as geology and material science. Although our review predominantly centers on biomedical applications, the algorithms and principles outlined here are equally appli

关键词： deep learning open-source code-free microscopy image analysis artificial intelligence

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A hardware accelerator to support deep learning processor units in real-time image processing

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 145卷

作者： Cittadini, Edoardo Marinoni, Mauro Buttazzo, Giorgio Scuola Super Sant Anna Pisa Italy

Deep neural networks are becoming crucial in many cyber-physical systems involving complex perceptual tasks. For those embedded systems requiring real-time interactions with dynamic environments, as autonomous robots and drones, it is of paramount importance that such algorithms are efficiently executed onboard on properly designed hardware accelerators to meet the required performance specifications. In particular, some neural network architectures for object detection and tracking, as You Only Look Once (YOLO), include heavy computational stages that need to be executed before and after the model inference. Such stages are typically not incorporated in traditional accelerators and are executed on general-purpose processors, thus introducing a bottleneck in the overall processing pipeline. To overcome such a problem, this paper presents a generalpurpose accelerator on a field-programmable gate array (FPGA) able to run pre-processing and post-processing operations typically required by vision tasks. The proposed solution has been tested in combination with a YOLO object detector accelerated on an Advanced Micro Devices (AMD) Xilinx Kria KR260 board mounting an UltraScale+ multiprocessor system-on-chip, achieving a significant improvement in terms of both timing performance and power consumption, and enabling onboard visual processing into drones. The proposed solution is able to boost the traditional object detection process by a factor of 4.4, allowing the execution of the full processing pipeline at 60 frames per second (fps), versus 13.6 fps reachable without the proposed accelerator. Asa result, this work enables the use of high-speed cameras for developing more reactive systems that can respond to incoming events with lower latency.

关键词： Hardware acceleration Object detection Deep neural networks Real-time processing image pre-processing acceleration Deep processing unit

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Novel virtual sensors development based on machine learning combined with convolutional neural-network image processing-translation for feedback control systems of internal combustion engines

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APPLIED ENERGY 2024年 365卷

作者： Sok, Ratnak Jeyamoorthy, Arravind Kusaka, Jin Waseda Univ Prop & Energy Syst Lab Tokyo 1698555 Japan

Physical sensors are commonly used to record performance data of internal combustion engines (ICEs) for online feedback control and calibration, but they are prone to diagnostic and increased development costs. Lookup tables are commonly used in conventional calibration and feedback control;however, the table parameters increase with the advancement of ICE technologies under transient operations. Consequently, the calibration and control systems are time-consuming. This work proposes novel virtual sensors to address these issues by predicting the combustion, performance, and emission of ICEs using neural networks and image processing/translation. The novel sensors are targeted for onboard feedback control systems under transient driving. Firstly, a virtual diesel engine (VDE) was developed and calibrated against experimental data taken from a production 2.2 L turbocharged diesel engine. The VDE was calibrated under WLTC, JC08, and NEDC transient operations and was used to generate teaching data. Next, the virtual sensors are developed using five machine learning (ML) regressors. The result shows that the coefficient of determination R-2 from all ML regressors exceeded 0.94, and the XG-Boost outperforms other ML techniques with R-2 > 0.977. XG-Boost parameter estimations were 8 times faster than that on a desktop simulation. Then, an image classification model using a deep convolutional neural network (D-CNN) is constructed, and the dependency of performance parameters and exhaust emissions with the rate of heat release (R.H.R) and in-cylinder pressure profile is confirmed. The performance parameters and emissions dependency was compared individually with R.H.R. and the in-cylinder pressure profile. As a result, a strong correlation between the performance and R.H.R. was observed. Finally, a generative adversarial network (GAN) model was constructed to translate the in-cylinder pressure profile to R.H.R. profile. A novel method to develop virtual sensors for advanced

关键词： Virtual sensors image processing-translation Engine feedback control ML -based Regressions

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Diagnosing the spores of tomato fungal diseases using microscopic image processing and machine learning

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第26期83卷 67283-67301页

作者： Javidan, Seyed Mohamad Banakar, Ahmad Vakilian, Keyvan Asefpour Ampatzidis, Yiannis Rahnama, Kamran Tarbiat Modares Univ Dept Biosyst Engn Tehran Iran Gorgan Univ Agr Sci & Nat Resources Dept Biosyst Engn Gorgan Iran Univ Florida Southwest Florida Res & Educ Ctr Agr & Biol Engn Dept 2685 FL-29 Immokalee FL 34142 USA Gorgan Univ Agr Sci & Nat Resources Fac Plant Prod Dept Plant Protect Gorgan Iran

Accurate diagnosis of plant diseases by the assessment of pathogen presence to reduce disease-related production loss is one of the most fundamental issues for farmers and specialists. This will improve product quality, increase productivity, reduce the use of fungicides, and reduce the final cost of agricultural production. Today, new technologies such as image processing, artificial intelligence, and deep learning have provided reliable solutions in various fields of precision agriculture and smart farm management. In this research, microscopic image processing and machine learning have been used to identify the spores of four common tomato fungal diseases. A dataset including 100 microscopic images of spores for each disease was developed, followed by the extraction of the texture, color, and shape features from the images. The classification results using random forest revealed an accuracy higher than 98%. Besides, as a reliable feature selection algorithm, the butterfly optimization algorithm (BOA) was used to detect the effective image features to identify and classify diseases. This algorithm recognized image textural features as the most effective features in the diagnosis and classification of disease spores. Considering only the eight most effective features selected with BOA resulted in an accuracy of 95% in disease detection. To further investigate the performance of the proposed method, its accuracy was compared with the accuracies of convolutional neural networks and EfficientNet as two reliable deep learning algorithms. Not only the prediction accuracy of these methods was not favorable (65 and 83.55%, respectively), they were very time-consuming. According to the findings, the proposed framework has high reliability in disease diagnosis and can help in the management of tomato fungal diseases.

关键词： Artificial intelligence Butterfly optimization algorithm Disease diagnosis Microscopic image processing Morphological features Tomato disease spores

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Identification of Locations in Mecca using image Pre-processing, neural networks and Deep learning

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第9期49卷 12091-12111页

作者： Taha, Mohammed Abdulghani Sah, Melike Direkoglu, Cem Tishk Int Univ Comp Engn Dept Erbil Iraq Cyprus Int Univ Comp Engn Dept North Cyprus Via Mersin 10 Nicosia Turkiye Middle East Tech Univ Elect & Elect Engn Northern Cyprus CampusNorth Cyprus Via Mersin 10 Guzelyurt Turkiye

Every year, more than two million Muslim pilgrims from all over the world visit Mecca to perform Hajj worship. The challenge of location identification in dense crowds is significant, leading to dangerous consequences such as injury or loss. Existing works for person localization remain challenged, especially in crowded places like Mecca during Hajj. In this work, we propose a novel location identification method using image pre-processing and different machine learning classifiers, with the creation of a new image dataset for hotspot locations in Mecca. image pre-processing algorithms are applied to enhance the geographic information present in the images, and the obtained features are classified using CNN, ANN, and SVM classifiers. Extensive evaluations reveal that the proposed pre-processing algorithm with CNN achieves the best localization performance with an accuracy of 90%, followed by ANN with an accuracy of 84%, and SVM with an accuracy of 80.50%. Without pre-processing, the accuracies are significantly lower: 63% for CNN, 73% for ANN, and 71.50% for SVM. In addition, the proposed approach was compared with other deep learning models, VGG16, AlexNet and ResNet50 on our dataset achieving an accuracy of 61%, 65% and 62%, respectively. Results demonstrate the effectiveness of our proposed method in comparison with other deep transfer learning methods on a small dataset, offering promising solutions for crowded place navigation.

关键词： Artificial Intelligence image processing Deep learning Convolutional neural Network Place recognition Person localization

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The application of machine learning algorithms in image processing denoising 4

The application of machine learning algorithms in image proc...

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4th International Conference on Advanced algorithms and neural networks, AANN 2024

作者： Lu, Di Xiong, Zhengqiang Wuhan Business University WuHan HuBei430000 China

ISBN: (纸本)9781510686106

In order to solve the problem of losing the original information of images in traditional image processing methods during denoising, this paper proposes an application method of machine learning algorithm in image processing denoising. This also means that it is necessary to continuously compare the processed image with the original image, which will increase the time required for denoising. However, deep learning techniques have played an important role in this regard. Through intelligent technology, especially supervised machine learning algorithms, information images can be better processed, enabling people to observe things more clearly and take correct actions. Specifically, we can use supervised machine learning algorithms to extract noise during image denoising. This method is based on setting separation nodes for image noise at multiple points, and then using clustering theory to filter out noise, thereby completing image denoising. The experimental results show that using animal images as the test object, we can detect the noise contained in them, and by comparing the effects of the original method and the new method, we found that the new method can obtain results consistent with the original image. More importantly, without introducing image distortion, the new method controls the denoising time to 1.15 seconds, while the original method takes an average of 20.21 to 15.13 seconds. This indicates that the new method can significantly improve the efficiency of denoising and has the potential for practical applications. © 2024 SPIE.

关键词： Contrastive learning

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A Comprehensive Survey of Methods for Identifying Counterfeit Banknotes Using image processing and machine learning 6th

A Comprehensive Survey of Methods for Identifying Counterfei...

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6th International Conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Sharmila, B. Sowmiya, A. Mary, Teena Sandeep, J. Sreeja, C.S. CHRIST University Dharmaram College Post Hosur Road Karnataka Bengaluru560029 India

ISBN: (纸本)9789819783281

The world economy is threatened by counterfeit currencies. Counterfeit currencies are often difficult, time-consuming and ineffective to identify manually. Automated methods based on image processing techniques and machine learning algorithms are helpful in detecting counterfeit notes. This survey paper reviews the current strategies on fake banknote detection using image processing techniques and machine learning algorithms. We discuss various stages of the detection process, including image acquisition, preprocessing, feature extraction and classification. Furthermore, we analyze the limitations and comparative performance of different algorithms and approaches mentioned in the literature. The survey aims to provide insights into the various methodologies, challenges and future directions in the field of fake banknote detection, facilitating the development of more robust and effective counterfeit detection systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Enhanced image processing for Improved Detection and Analysis of COVID-19 in Medical Imaging by U sing machine learning algorithms 8

Enhanced Image Processing for Improved Detection and Analysi...

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8th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2024

作者： Albadwi, Sinan Ghanem Mikhwir Islamic Azad university Isfahan Branch Khorasgan Iran College of Engineering Al-Ayen University Artificial Intelligence Engineering Department Thi-Qar Iraq

ISBN: (纸本)9798350354423

This work intends to increase COVID-19 identification and analysis utilizing modern image processing methods and machine learning algorithms. We deployed a dataset including 5,000 chest X-ray pictures from the COVID-19 image Data Collection and performed numerous preprocessing methods, including noise reduction and normalization, to prepare the data. For machine learning, we deployed a convolutional neural network (CNN) with a model architecture of 3 convolutional layers and 2 dense layers. Our CNN model obtained an outstanding accuracy of 94.7% in recognizing COVID-19 patients from the X-ray pictures. This high accuracy illustrates the efficiency of merging modern image processing techniques with machine learning to increase the diagnostic precision for COVID-19, possibly assisting in more rapid and precise illness identification. © 2024 IEEE.

关键词： Convolutional neural networks

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LDIS: Leaf Disease Identification System Using image processing and machine learning

LDIS: Leaf Disease Identification System Using Image Process...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Negi, Heena Kanyal, Rajni Parsola, Jyoti Pandey, Neeraj Kumar School of Computing Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Dept. of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350379716

Leaf diseases can have a considerable influence on crop production and food security. Therefore, it's crucial to detect and diagnose these diseases early to prevent their spread and minimize yield losses. image processing (IP), machine-learning (ML) algorithms and Deep learning (DL) have been demonstrated to be effective and precise tools for plant disease detection. This research proposes a system Leaf Disease Identification System (LDIS) for detection of plant leaf diseases using Convolutional neural networks (CNNs). image processing technique Gaussian Mixture Model is used for segmentation of disease affected region in potato leaves. We have also proposed our shape-extraction algorithm for extraction of shape feature of a leaf. Moreover, the performance is tested and trained in standard publicly available dataset. The accuracy achieved is 96.58%. Ultimately, the integration of image processing and machine learning algorithms presents a promising solution for timely and accurate detection of plant leaf diseases, which can aid in the early detection, management and control of these diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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