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Can Deep learning Outperform Traditional Methods in Upscaling IR images for Solar Irradiance Estimation? 26

Can Deep Learning Outperform Traditional Methods in Upscalin...

引用

26th Irish machine Vision and image processing conference, IMVIP 2024

作者： Hossain, Md Yearat Nijhum, Ifran Rahman Rakib, Md Mahbub Hasan Dev, Soumyabrata The ADAPT SFI Research Centre Ireland School of Computer Science University College Dublin Ireland Department of Electrical and Computer Engineering North South University Bangladesh

ISBN: (纸本)9781837242672

Infrared imaging provides crucial information in the infrared spectrum, invisible to standard visible-light imaging systems. However, high-resolution infrared imaging is challenging and expensive, limiting its applications. Deep learning, particularly Convolutional neural networks (CNNs), has significantly advanced image processing but relies on high-resolution images for optimal performance. To address the difficulty of acquiring high-resolution infrared images, image upscaling techniques, including traditional interpolation and modern deep learning methods, can be used. This study compares these methods using the GIRASOL dataset for solar irradiance estimation. We evaluated bicubic, bilinear, and nearest neighbor interpolation against deep learning image upscaling models SwinIR and RealESRGAN. Despite their success in upscaling visible spectrum images, these models did not effectively enhance low-resolution infrared images, resulting in less accurate CNN predictions for solar irradiance compared to traditional methods. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Convolutional neural networks

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Real-Time Scratched image Restoration Using neural networks 2

Real-Time Scratched Image Restoration Using Neural Networks

引用

2nd International conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Sharma, Harsh Pradhan, Rajeshwari Mogha, Harsh Singh, Shivpratap Kumar, Rupesh Computer Science and Engineering Chandigarh University Punjab India

ISBN: (纸本)9798331519056

Real-time image restoration is a cutting-edge tech-nology that accelerates or rebuilds images as soon as they are captured, processed, or released to correct issues such as noise, blur, and compression. This field is of great importance in applications such as medical imaging, surveillance, autonomous vehicles. and live video streaming where sharpness and image quality are fundamental The main challenge lies in developing algorithms that can quickly and efficiently restore image quality. It balances the need for high-speed processing with the need for precision and adaptability to different conditions. Advanced techniques Including deep learning and convolutional neural networks (CNN) are often used to achieve these goals. Moreover, it is essential to optimize these algorithms for real-time pro-cessing. Real-time performance in constrained hardware such as mobile devices or embedded systems As real-time image recovery technology continues to develop, It thus promises to significantly improve the quality of image data in a variety of domains. They provide sharper, more reliable images for critical applications. © 2024 IEEE.

关键词： Convolutional neural networks

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

23rd International Conference on Intelligent Systems Design ...

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

ISBN: (纸本)9783031648359

The proceedings contain 47 papers. The special focus in this conference is on Intelligent Systems Design and applications. The topics include: machine learning Techniques for Pancreatic Cancer Detection;integrating Artificial Intelligence and Data Analytics for Enhanced Healthcare Management: Innovations and Challenges;an Intelligent Model for Post Covid Hearing Loss;medical Reports Simplification Using Large Language Models;analysis of Magnetic Resonance Imaging for Parkinson's Disease;study on Health Issue Identification Using Deep learning and Convolutional neural networks;early-Stage Lung Cancer Prediction: A machine learning Approach;convolutional neural Network-Based Brain Tumor Segmentation Using Detectron2;Deep learning-Based Histopathological Analysis for Colon Cancer Diagnosis: A Comparative Study of CNN and Transformer Models with image Preprocessing Techniques;Detecting Parkinson's Disease at an Early Stage Through machine learning Analysis of Brain MRI images;early-Stage Cervical Cancer Detection via Ensemble learning and image Feature Integration;comprehensive Comparative Analysis of Breast Cancer Forecasting Using machine learning Algorithms and Feature Selection Methods;classification of Arrhythmia Using Deep learning;an Integrated machine learning and IoT Based Approach for Enhanced Healthcare Efficiency and Personalized Treatment;ceLaTis: A Large Scale Multimodal Dataset with Deep Region Network to Diagnose Cervical Cancer;a Deep learning Approach With Sparse Autoencoder for Alzheimers Disease Classification;an Improved Gradient Based Joint Histogram Equalization Technique for Mammogram image Contrast Enhancement;comparative Performance Analysis of Deep learning Models in Cervical Cancer Detection;comparative Analysis for Feature Selection Approaches for Parkinson’s Disease Prediction;autism Spectrum Disorder Prediction: A machine learning Approach;arterial Disease Prediction in Inflammatory Bowel Disease Patients.

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

23rd International Conference on Intelligent Systems Design ...

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

ISBN: (纸本)9783031646492

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

23rd International Conference on Intelligent Systems Design ...

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

ISBN: (纸本)9783031648120

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

23rd International Conference on Intelligent Systems Design ...

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

ISBN: (纸本)9783031648465

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

23rd International Conference on Intelligent Systems Design ...

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

ISBN: (纸本)9783031647758

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

23rd International Conference on Intelligent Systems Design ...

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

ISBN: (纸本)9783031648496

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

23rd International Conference on Intelligent Systems Design ...

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23rd International conference on Intelligent Systems Design and applications, ISDA 2023

ISBN: (纸本)9783031647789

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Using Cropped Datasets for training Deep neural networks for face recognition 31

Using Cropped Datasets for training Deep Neural Networks for...

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31st International conference on Systems, Signals and image processing (IWSSIP)

作者： Quintella, Felipe Ferreira Ganacim, Francisco Hurtado, Jan Raposo, Alberto Pontif Catholic Univ Rio De Janeiro Dept Informat & Tecgraf Inst Rio De Janeiro Brazil

ISBN: (纸本)9798350391893;9798350391886

Face recognition is a fundamental problem in numerous real-world applications, and it can be tackled using classification deep learning models. This kind of solution requires extensive datasets to represent the target subjects, where maintaining their privacy is essential. Further, there are challenging scenarios where the face of the subject can be partially occluded, e.g. a subject wearing a mask, making it difficult the recognition process. In this paper, extensive experiments are presented for deep learning-based face recognition, considering different dataset settings. More precisely, cropping operations are proposed to partially represent the face focusing on the eyes and nose region for model training, minimizing privacy issues, storage space, and allowing the trained model to process partially occluded faces. Two publicly available datasets named VGG-Face and DigiFace-1M are adapted for evaluation, and five convolutional neural network models are used for comparison, including ResNet, VGG-16, AlexNet, DenseNet-169, and EfficientNetV2-M. The results suggest that, although this cropping operation impacts the accuracy of the model when compared to the processing of full faces, it can be viable solution to minimize the pointed issues while preserving acceptable performance.

关键词： face recognition deep learning classification crop

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