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1st International conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

ISBN: (纸本)9783031472237

The proceedings contain 46 papers. The special focus in this conference is on Advanced Computing, Machine Learning, Robotics and Internet Technologies. The topics include: Galactic Simulation: visual Perception of Anisotropic Dark Matter;Protocol Anomaly Detection in IIoT;Agricultural Informatics & ICT: The Foundation, Issues, Challenges and Possible Solutions—A Policy Work;a Guava Leaf Disease Identification Application;text to image Generation Using Attentional Generative Adversarial Network;attention-CoviNet: A Deep-Learning Approach to Classify Covid-19 Using Chest X-Rays;a Deep Learning Framework for Violence Detection in Videos Using Transfer Learning;multi-focus image Fusion Methods: A Review;cache Memory and On-Chip Cache Architecture: A Survey;authenticating Smartphone Users Continuously Even if the Smartphone is in the User’s Pocket;Comparative Analysis of Machine Learning Algorithms for COVID-19 Detection and Prediction;machine Learning Classifiers Explanations with Prototype Counterfactual;a Systematic Study of Super-Resolution Generative Adversarial Networks: Review;Stance Detection in Manipuri Editorial Article Using CRF;deep Learning Based Software Vulnerability Detection in Code Snippets and Tag Questions Using Convolutional Neural Networks;a Comprehensive Study of the Performances of Imbalanced Data Learning Methods with Different Optimization Techniques;Smart Parking System Using Arduino and IR Sensor;quMaDe: Quick Foreground Mask and Monocular Depth Data Generation;fine-Grained Air Quality with Deep Air Learning;enhancing Melanoma Skin Cancer Detection with Machine Learning and image processing Techniques;image processing Technique and SVM for Epizootic Ulcerative Syndrome Fish image Classification.

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1st International conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

1st International Conference on Advanced Computing, Machine ...

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1st International conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

ISBN: (纸本)9783031472206

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Enhanced image Deblurring with the Fusion of Generative Adversarial Networks and Transformer Models 1st

Enhanced Image Deblurring with the Fusion of Generative Adve...

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1st International conference on Innovation and Emerging Trends in Computing and Information Technologies, IETCIT 2024

作者： Ranjan, Arti Ravinder, M. New Delhi India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida India

ISBN: (纸本)9783031808418

In the domain of image processing, deblurring remains a challenging task, particularly in restoring high-fidelity details from blurred images. This paper introduces a novel approach that synergizes the strengths of Generative Adversarial Networks (GANs) and Transformer models to address this challenge. Our methodology leverages the generative capability of GANs to produce realistic and sharp images, while incorporating the Transformer’s prowess in capturing long-range dependencies and intricate details within images. This adversarial training regime is designed to refine the model’s ability to generate deblurred images that are not only accurate in detail but also high in perceptual quality. Through extensive experiments on various datasets, our model demonstrates superior performance in deblurring, outperforming existing state-of-the-art methods in terms of both objective metrics and subjective visual quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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MRIQA: Subjective Method and Objective Model for Magnetic Resonance image Quality Assessment

MRIQA: Subjective Method and Objective Model for Magnetic Re...

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IEEE visual communications and image processing (VCIP)

作者： Qi Chen Fang Liu Huiyu Duan Yao Wang Xiongkuo Min Yan Zhou Guangtao Zhai Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai China Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781665475938

Magnetic Resonance Imaging (MRI) is widely used for medical diagnosis, staging and follow-up of disease. However, MRI images may have artifacts due to various reasons such as patient movement or machine distortion, which may be unintentionally introduced during the procedure of medical image acquisition, processing, etc. These artifacts may affect the effectiveness of diagnosis or even cause false diagnosis. To solve this problem, we propose a general medical image quality assessment (MIQA) methodology, including subjective MIQA procedures and objective MIQA algorithms. We further apply this methodology to MRI images in this paper due to its widespread use in practical applications. We first establish a magnetic resonance imaging quality assessment (MRIQA) database, which contains 3809 MRI images. Then a subjective image quality assessment experiment is conducted by expert doctors according to the diagnostic value of these images, which split all MRI images into 1285 low quality images and 2524 high quality images. We then conduct a baseline deep learning experiment, and propose an attention based MIQANet model to automatically separate MRI images into high quality and low quality based on their diagnosis value. Our proposed method achieves a great quality assessment accuracy of 96.59%. The constructed MRIQA database and proposed MIQA model will be public available to further promote medical IQA research.

关键词： image quality Deep learning Databases visual communication Magnetic resonance imaging Neural networks Magnetic resonance

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Single-image Real-Time Rain Removal Based on Depth-Guided Non-Local Features

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IEEE TRANSACTIONS ON image processing 2021年 30卷 1759-1770页

作者： Hu, Xiaowei Zhu, Lei Wang, Tianyu Fu, Chi-Wing Heng, Pheng-Ann Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Key Lab Virtual Real & Human Interact Te Shenzhen 518055 Peoples R China

Rain is a common weather phenomenon that affects environmental monitoring and surveillance systems. According to an established rain model (Garg and Nayar, 2007), the scene visibility in the rain varies with the depth from the camera, where objects faraway are visually blocked more by the fog than by the rain streaks. However, existing datasets and methods for rain removal ignore these physical properties, thus limiting the rain removal efficiency on real photos. In this work, we analyze the visual effects of rain subject to scene depth and formulate a rain imaging model that collectively considers rain streaks and fog. Also, we prepare a dataset called RainCityscapes on real outdoor photos. Furthermore, we design a novel real-time end-to-end deep neural network, for which we train to learn the depth-guided non-local features and to regress a residual map to produce a rain-free output image. We performed various experiments to visually and quantitatively compare our method with several state-of-the-art methods to show its superiority over others.

关键词： Rain removal rain formulation depth-guided non-local features

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A Weighted Mean Absolute Error Metric for image Quality Assessment

A Weighted Mean Absolute Error Metric for Image Quality Asse...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Hao, Sihan Li, Sumei Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728180687

Pixel-wise image quality assessment (IQA) algorithms, such as mean square error (MSE), mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) correlate well with perceptual quality when dealing with images sharing the same distortion type but not well when processing images in different distortion types, which is inconsistent with human visual system (HVS). Although a large number of metrics based on image error has been proposed, there are still difficulties and limitations. To solve this problem, a full reference image quality assessment (FR-IQA) method based on MAE is proposed in this paper. The metric divides the image error (difference between distorted image and reference image) map into smooth region and texture-edge region, calculates their mean values respectively, and then gives them different weights considering the masking effect. The key innovation of this paper is to propose a distortion significance measurement, which is a visual quality coefficient that can effectively indicate the influence of different distortion types on perceptual quality and unify them with HVS. The segmented image error maps are weighted by the distortion significance coefficient. The experimental results on four largest benchmark databases show that the most of the distortions are successfully evaluated and the results are consistent with HVS.

关键词： Error map MAE FR-IQA distortion significance coefficient

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No-Reference Stereoscopic image Quality Assessment Based On visual Attention Mechanism

No-Reference Stereoscopic Image Quality Assessment Based On ...

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IEEE International conference on visual communications and image processing (VCIP)

作者： Li, Sumei Zhao, Ping Chang, Yongli Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728180687

In this paper, we proposed an optimized model based on the visual attention mechanism(VAM) for no-reference stereoscopic image quality assessment (SIQA). A CNN model is designed based on dual attention mechanism (DAM), which includes channel attention mechanism and spatial attention mechanism. The channel attention mechanism can give high weight to the features with large contribution to final quality, and small weight to features with low contribution. The spatial attention mechanism considers the inner region of a feature, and different areas are assigned different weights according to the importance of the region within the feature. In addition, data selection strategy is designed for CNN model. According to VAM, visual saliency is applied to guide data selection, and a certain proportion of saliency patches are employed to fine tune the network. The same operation is performed on the test set, which can remove data redundancy and improve algorithm performance. Experimental results on two public databases show that the proposed model is superior to the state-of-the-art SIQA methods. Cross-database validation shows high generalization ability and high effectiveness of our model.

关键词： stereoscopic image visual attention mechanism dual attention mechanism data selection

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Cleaning Highly Unbalanced Multisource image Dataset for Quality Control in Cervical Precancer Screening 4th

Cleaning Highly Unbalanced Multisource Image Dataset for Qua...

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4th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2021

作者： Xue, Zhiyun Guo, Peng Angara, Sandeep Pal, Anabik Jeronimo, Jose Desai, Kanan T. Ajenifuja, Olusegun K. Adepiti, Clement A. Sanjose, Silvia D. Schiffman, Mark Antani, Sameer National Library of Medicine BethesdaMD20894 United States National Cancer Institute RockvilleMD20850 United States Obafemi Awolowo University Ile-Ife Nigeria

ISBN: (纸本)9783031070044

Automated visual evaluation (AVE) of uterine cervix images is a deep learning algorithm that aims to improve cervical pre-cancer screening in low or medium resource regions (LMRR). image quality control is an important pre-step in the development and use of AVE. In our work, we use data retrospectively collected from different sources/providers for analysis. In addition to good images, the datasets include low-quality images, green-filter images, and post Lugol’s iodine images. The latter two are uncommon in VIA (visual inspection with acetic acid) and should be removed along with low-quality images. In this paper, we apply and compare two state-of-the-art deep learning networks to filter out those two types of cervix images after cervix detection. One of the deep learning networks is DeepSAD, a semi-supervised anomaly detection network, while the other is ResNeSt, an improved variant of the ResNet classification network. Specifically, we study and evaluate the algorithms on a highly unbalanced large dataset consisting of four subsets from different geographic regions acquired with different imaging device types. We also examine the cross-dataset performance of the algorithms. Both networks can achieve high performance (accuracy above 97% and F1 score above 94%) on the test set. © Springer Nature Switzerland AG 2022.

关键词： Iodine

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Low Light RAW image Enhancement Using Paired Fast Fourier Convolution and Transformer

Low Light RAW Image Enhancement Using Paired Fast Fourier Co...

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IEEE visual communications and image processing (VCIP)

作者： Yichi Zhang Hengyu Liu Dandan Ding Zhan Ma School of Information Science and Technology Hangzhou Normal University Hangzhou China School of Electronic Science and Engineering Nanjing University Nanjing China

ISBN: (纸本)9781665475938

Compared to RGB images non-linearly mapped from RAW data through the image Signal Processor (ISP), RAW data are linear to scene radiance and contain more native information, which is better to be modeled in many vision tasks. This work proposes to enhance low-light images in the RAW domain via a cross-scale framework using paired Fast Fourier Convolution (FFC) and Transformer, driving the network to characterize images effectively. The entire framework has three scales to abstract low-level, mid-level, and high-level representations of input images. We embed paired FFC and Transformer in each scale to attain spatial-spectral information extraction and aggregation. Specifically, by transforming features from the spatial domain into the spectral domain with FFC, pixel correlations can be effectively exploited locally and globally, generating representative features for the input image. Immediately, the Transformer using multi-head self-attention mechanism is applied to aggregate and embed important features. Experimental results demonstrate that our method significantly outperforms state-of-the-art low-light enhancement works in both full reference assessment metrics, including PSNR, MPSNR, and SSIM, and no-reference metrics, such as NIMA. Meanwhile, the perceptual quality of the proposed method is more visually pleasing than that of other methods.

关键词： Measurement Convolution visual communication Transforms Transformers Feature extraction Information retrieval

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Eye Movement Analysis using Virtual Reality in Early Detection of Alzheimer’s Disease

Eye Movement Analysis using Virtual Reality in Early Detecti...

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image processing, Computer Vision and Machine Learning (ICICML), International conference on

作者： Haiwei Zuo King Hann Lim Eswaran Sivaraman Jonathan Then Sien Phang Shenzhen City Polytechnic School of Information and Communications Technology Curtin Malaysia Research Institute Curtin University Malaysia

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Early detection of Alzheimer’s disease (AD) is important at the initial stage to provide prior treatment and control. In traditional diagnostic methods, medical doctors perform manual AD checking by asking patients to repeat several round of text reading and game exercises. The quantification of this manual assessment can be subjective due to the profession experience and it requires a further verification using the brain imaging. In this paper, an automated digital AD assessment method using Virtual Reality (VR) console is proposed to evaluate patient’s condition by tracking their eye movement during text reading. First, the eye movement pattern is captured using VR console during text reading. The recorded eye movement is then converted into frequency domain to study its frequency components during reading process. The simulation of AD symptom is set up by prompting subjects to read their familiar native language and unfamiliar language. As a result, the experiment shows that there are significant differences in eye movement patterns between native and unfamiliar language reading. The group of subjects reading unfamiliar language have random eye movement in less efficient visual processing. This pattern is similar to the patients who suffer in the AD symptom. In addition, the frequency features are extracted to study its correlations with reading comprehension performance in order to detect the early AD condition.

关键词： visualization Correlation Tracking Frequency-domain analysis Virtual reality Manuals Frequency conversion Feature extraction Alzheimer's disease Medical diagnostic imaging

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