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Articles de revues sur le sujet « Deep image »

Auteur : Grafiati

Publié le 7 juin 2025

Mis à jour le 2 août 2025

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1

Sravani, L., N. Rama Venkat Sai, K. Noomika, M. Upendra Kumar, and K. V. Adarsh. "Image Enhancement of Underwater Images using Deep Learning Techniques." International Journal of Research Publication and Reviews 4, no. 4 (2023): 81–86. http://dx.doi.org/10.55248/gengpi.2023.4.4.34620.

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Yao, Yao, Liqiang Han, Ben Fan, Dan Wang, and Wei Fan. "Image Target Recognition Based on Deep Learning." Open Access Journal of Astronomy 3, no. 1 (2025): 1–8. https://doi.org/10.23880/oaja-16000160.

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Target recognition image is of great significance to the acquisition of ground and sea targets in the synthetic aperture radar (SAR) field. It has become a hot issue to realize automatic target detection and improve the accuracy of target recognition. In order to accurately obtain target information in images and solve the problem of over-fitting in deep neural network training, this study applied SAR image iterative denoising based on non-local adaptive dictionary to process SAR images, and constructed CNN network to extract SAR image features. Experimental results show that the proposed meth

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Shin, Chang Jong, Tae Bok Lee, and Yong Seok Heo. "Dual Image Deblurring Using Deep Image Prior." Electronics 10, no. 17 (2021): 2045. http://dx.doi.org/10.3390/electronics10172045.

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Blind image deblurring, one of the main problems in image restoration, is a challenging, ill-posed problem. Hence, it is important to design a prior to solve it. Recently, deep image prior (DIP) has shown that convolutional neural networks (CNNs) can be a powerful prior for a single natural image. Previous DIP-based deblurring methods exploited CNNs as a prior when solving the blind deburring problem and performed remarkably well. However, these methods do not completely utilize the given multiple blurry images, and have limitations of performance for severely blurred images. This is because t

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Cannas, Edoardo Daniele, Sara Mandelli, Paolo Bestagini, Stefano Tubaro, and Edward J. Delp. "Deep Image Prior Amplitude SAR Image Anonymization." Remote Sensing 15, no. 15 (2023): 3750. http://dx.doi.org/10.3390/rs15153750.

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This paper presents an extensive evaluation of the Deep Image Prior (DIP) technique for image inpainting on Synthetic Aperture Radar (SAR) images. SAR images are gaining popularity in various applications, but there may be a need to conceal certain regions of them. Image inpainting provides a solution for this. However, not all inpainting techniques are designed to work on SAR images. Some are intended for use on photographs, while others have to be specifically trained on top of a huge set of images. In this work, we evaluate the performance of the DIP technique that is capable of addressing

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Manoj krishna, M., M. Neelima, M. Harshali, and M. Venu Gopala Rao. "Image classification using Deep learning." International Journal of Engineering & Technology 7, no. 2.7 (2018): 614. http://dx.doi.org/10.14419/ijet.v7i2.7.10892.

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The image classification is a classical problem of image processing, computer vision and machine learning fields. In this paper we study the image classification using deep learning. We use AlexNet architecture with convolutional neural networks for this purpose. Four test images are selected from the ImageNet database for the classification purpose. We cropped the images for various portion areas and conducted experiments. The results show the effectiveness of deep learning based image classification using AlexNet.

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Berrahal, Mohammed, Mohammed Boukabous, Mimoun Yandouzi, Mounir Grari, and Idriss Idrissi. "Investigating the effectiveness of deep learning approaches for deep fake detection." Bulletin of Electrical Engineering and Informatics 12, no. 6 (2023): 3853–60. http://dx.doi.org/10.11591/eei.v12i6.6221.

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As a result of notable progress in image processing and machine learning algorithms, generating, modifying, and manufacturing superior quality images has become less complicated. Nonetheless, malevolent individuals can exploit these tools to generate counterfeit images that seem genuine. Such fake images can be used to harm others, evade image detection algorithms, or deceive recognition classifiers. In this paper, we propose the implementation of the best-performing convolutional neural network (CNN) based classifier to distinguish between generated fake face images and real images. This pape

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Park, Ingyu, and Unjoo Lee. "Automatic, Qualitative Scoring of the Clock Drawing Test (CDT) Based on U-Net, CNN and Mobile Sensor Data." Sensors 21, no. 15 (2021): 5239. http://dx.doi.org/10.3390/s21155239.

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The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular screening tool for cognitive functions. In spite of its qualitative capabilities in diagnosis of neurological diseases, the assessment of the CDT has depended on quantitative methods as well as manual paper based methods. Furthermore, due to the impact of the advancement of mobile smart devices imbedding several sensors and deep learning algorithms, the necessity of a standardized, qualitative, and automatic scoring system for CDT has been increased. This study presents a mobile phone application, mCDT, for the CDT and suggests

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Kweon, Hyeokjoon, Jinsun Park, Sanghyun Woo, and Donghyeon Cho. "Deep Multi-Image Steganography with Private Keys." Electronics 10, no. 16 (2021): 1906. http://dx.doi.org/10.3390/electronics10161906.

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In this paper, we propose deep multi-image steganography with private keys. Recently, several deep CNN-based algorithms have been proposed to hide multiple secret images in a single cover image. However, conventional methods are prone to the leakage of secret information because they do not provide access to an individual secret image and often decrypt the entire hidden information all at once. To tackle the problem, we introduce the concept of private keys for secret images. Our method conceals multiple secret images in a single cover image and generates a visually similar container image con

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D.Rathna, Kishore, D.Suneetha, Babu P.Narendra, and P.Chinababu. "Deep Convolutional Neural Network based Image Steganogrpahy Technique for Audio-Image Hiding Algorithm." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 2187–89. https://doi.org/10.35940/ijeat.D7843.049420.

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Steganography is one expanding filed in the area of Data Security. Steganography has attractive number of application from a vast number of researchers. The most existing technique in steganogarphy is Least Significant Bit (LSB) encoding. Now a day there has been so many new approaches employing with different techniques like deep learning. Those techniques are used to address the problems of steganography. Now a day’s many of the exisiting algorithms are based on the image to data, image to image steganography. In this paper we hide secret audio into the digital image with the help of d

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Sharma, Puspad Kumar, Nitesh Gupta, and Anurag Shrivastava. "A Review on Deep Image Contrast Enhancement." SMART MOVES JOURNAL IJOSCIENCE 6, no. 1 (2020): 4. http://dx.doi.org/10.24113/ijoscience.v6i1.258.

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In image processing applications, one of the main preprocessing phases is image enhancement that is used to produce high quality image or enhanced image than the original input image. These enhanced images can be used in many applications such as remote sensing applications, geo-satellite images, etc. The quality of an image is affected due to several conditions such as by poor illumination, atmospheric condition, wrong lens aperture setting of the camera, noise, etc [2]. So, such degraded/low exposure images are needed to be enhanced by increasing the brightness as well as its contrast and th

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Karamanji, Awab Qasim, Asia S. Ahmed, and Ali F. Fadhil. "Comparative Deep Learning Models in Applications of Steganography Detection." Journal of Image and Graphics 12, no. 3 (2024): 312–19. http://dx.doi.org/10.18178/joig.12.3.312-319.

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This paper explores the use of deep learning algorithms in steganography detection. More specifically, it examines deep learning-based binary classification to distinguish between stego and non-stego images from the three steganography algorithms, The Wavelet Obtained Weights (WOW), Spatial Universal Wavelet Relative Distortion (S-UNIWARD), Highly Undetectable Steganography (HUGO). It also highlights the lack of research to develop a practical universal image steganography detection system using trained deep learning. The proposed farmwork combines multiple detection deep learning architecture

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Yu, Haichao, Ning Xu, Zilong Huang, Yuqian Zhou, and Humphrey Shi. "High-Resolution Deep Image Matting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (2021): 3217–24. http://dx.doi.org/10.1609/aaai.v35i4.16432.

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Image matting is a key technique for image and video editing and composition. Conventionally, deep learning approaches take the whole input image and an associated trimap to infer the alpha matte using convolutional neural networks. Such approaches set state-of-the-arts in image matting; however, they may fail in real-world matting applications due to hardware limitations, since real-world input images for matting are mostly of very high resolution. In this paper, we propose HDMatt, a first deep learning based image matting approach for high-resolution inputs. More concretely, HDMatt runs matt

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Kumar, Boda Nithin. "Image Forgery Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 7048–50. http://dx.doi.org/10.22214/ijraset.2023.52367.

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Abstract: In recent years, with the prevalence of cameras, taking pictures has become more and more popular. Images are essential to our daily life as they contain a wealth of information and often need to be enhanced to obtain additional information. Various tools are available to improve image quality. Nevertheless, they are also commonly used for fake images, leading to the spread of misinformation. This has increased the severity and frequency of image forgery, a major concern today. Many traditional techniques have been developed over time to detect fake images. Convolutional Neural Netwo

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Reddy, Ainala Vinay Kumar. "Image Deblurring using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 743–47. http://dx.doi.org/10.22214/ijraset.2023.50114.

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Abstract: Image deblurring is a most considered problem in low-level computer vision with an objective to recover a high resolution image from a blurred input image. It is a mandatory task in image processing and has been studied for several years which is used in reconstruction of images which are blurred due to various reasons. Particularly in these years, deep learning approaches have shown great promises in various image restoration tasks, which includes image deblurring. Among these, DBSRCNN is a powerful deep learning approach that has been used for super-resolution and image deblurring.

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Kaur, Er Sandeep. "Image Classification Using Deep Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–11. http://dx.doi.org/10.55041/ijsrem27484.

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- Image classification is an important topic of study in the field of image processing nowadays and is a popular area of research. By providing the computer with data to learn from, image categorization was created to close the gap between computer vision and human vision. In this paper, the methods for categorising images using traditional machine learning and deep learning are compared and investigated. This study employs a tensor flow framework and convolutional neural networks to classify images. This paper implements CNN in binary classification and multi-class classification for object i

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Krishna, TNVSD Murali. "Image Colorization Using Deep Learning." International Scientific Journal of Engineering and Management 03, no. 04 (2024): 1–9. http://dx.doi.org/10.55041/isjem01589.

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mage colorization using deep learning is a fascinating field that aims to add color to black and white images automatically. This project explores the use of advanced neural networks, specifically convolutional neural networks (CNNs), to achieve this task efficiently and accurately. By leveraging large datasets of color images paired with their black and white counterparts, the CNN learns to predict plausible colorizations for grayscale input images. The project demonstrates the effectiveness of deep learning techniques in recreating realistic colors while preserving the details of the origina

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Shruthi, Ch M., Vemullapalli Ramachandra Anirudh, Palla Bhargava Rao, Birru Shiva Shankar, and Akhilesh Pandey. "Deep Learning based Automated Image Deblurring." E3S Web of Conferences 430 (2023): 01052. http://dx.doi.org/10.1051/e3sconf/202343001052.

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Image deblurring is a challenging task that aims to restore a sharp and clear image from a blurred one. This problem is usually caused by camera motion or defocus blur. The objective of this paper is to develop a model that can effectively remove Gaussian blur from an image and improve its quality using deep learning techniques. Automated image deblurring is achieved using deep learning, this approach involves implementing a combination of convolutional neural networks (CNN) and simple auto encoders to train the model on a dataset of blurred and corresponding sharp images. The model is then us

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Li, Jike. "Representative Image Outpainting and Image Super-Resolution Methods Based on Deep Learning." Applied and Computational Engineering 80, no. 1 (2024): 18–28. http://dx.doi.org/10.54254/2755-2721/80/2024ch0055.

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The image generation based on deep learning is a technology that can generate new images or outpaint old images or improve visual effect of old images through learning from input data, according to deep learning structure. The representative technologies of image generation are image outpainting and image super-resolution. Deep learning is widely utilized in the field of computer vision. Facing different needs, it is essential to choose proper ways. This essay reviews several representative and new methods of image outpainting and image super-resolution. Compared with the results generated by

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Li, Jike. "Representative Image Outpainting and Image Super-Resolution Methods Based on Deep Learning." Applied and Computational Engineering 96, no. 1 (2024): 15–25. http://dx.doi.org/10.54254/2755-2721/96/2024ch0055.

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The image generation based on deep learning is a technology that can generate new images or outpaint old images or improve visual effect of old images through learning from input data, according to deep learning structure. The representative technologies of image generation are image outpainting and image super-resolution. Deep learning is widely utilized in the field of computer vision. Facing different needs, it is essential to choose proper ways. This essay reviews several representative and new methods of image outpainting and image super-resolution. Compared with the results generated by

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Ishaq, Omer, Sajith Kecheril Sadanandan, and Carolina Wählby. "Deep Fish." SLAS DISCOVERY: Advancing the Science of Drug Discovery 22, no. 1 (2016): 102–7. http://dx.doi.org/10.1177/1087057116667894.

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Zebrafish ( Danio rerio) is an important vertebrate model organism in biomedical research, especially suitable for morphological screening due to its transparent body during early development. Deep learning has emerged as a dominant paradigm for data analysis and found a number of applications in computer vision and image analysis. Here we demonstrate the potential of a deep learning approach for accurate high-throughput classification of whole-body zebrafish deformations in multifish microwell plates. Deep learning uses the raw image data as an input, without the need of expert knowledge for

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Khan, Sufiyan Ali. "IMAGE CAPTION GENERATOR USING DEEP LEARNING." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31987.

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Image Captioning is a task where each image must be understood properly and are able generate suitable caption with proper grammatical structure.Here it is a hybrid system which uses multilayer CNN (Convolutional Neural Network) for generating keywords which narrates given input images and Long Short Term Memory(LSTM) for precisely constructing the significant captions utilizing the obtained words .Convolution Neural Network (CNN) proven to be so effective that there is a way to get to any kind of estimating problem that includes image data as input. LSTM was developed to avoid the poor predic

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Pan, Jinshan, Yang Liu, Deqing Sun, et al. "Image Formation Model Guided Deep Image Super-Resolution." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11807–14. http://dx.doi.org/10.1609/aaai.v34i07.6853.

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We present a simple and effective image super-resolution algorithm that imposes an image formation constraint on the deep neural networks via pixel substitution. The proposed algorithm first uses a deep neural network to estimate intermediate high-resolution images, blurs the intermediate images using known blur kernels, and then substitutes values of the pixels at the un-decimated positions with those of the corresponding pixels from the low-resolution images. The output of the pixel substitution process strictly satisfies the image formation model and is further refined by the same deep neur

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K K, Mafnitha, and Anju K B. "Enhancing Image Security Using DNA and Deep Steganography." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43639.

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This project is aimed at providing a secure way of sending confidential images between users. Application is built to provide protection of the sensitive image being transmitted. Modern advancements have made it incredibly convenient to both send and store digital images online. Images often contain sensitive personal information, such as photographs, identifica- tion documents, medical records etc. Unauthorized access to these images can lead to identity theft, privacy invasion, and personal harm. Image security is crucial for several reasons, spanning personal privacy, corporate confidential

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Kwak, Deawon, Jiwoo Choi, and Sungjin Lee. "Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition." Sensors 23, no. 4 (2023): 2307. http://dx.doi.org/10.3390/s23042307.

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This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast cancer classification and by inferring their affected location. For this goal, the image recognition application strategies for the maximal diagnosis accuracy in each medical image data are investigated in terms of various image classification (VGGNet19, ResNet50, DenseNet121, EfficietNet v2), image segmentation (UNet, ResUNet++, DeepLab v3),

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Un, Cheng-Hin, and Ka-Cheng Choi. "Deep Watermarking Based on Swin Transformer for Deep Model Protection." Applied Sciences 15, no. 10 (2025): 5250. https://doi.org/10.3390/app15105250.

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This study improves existing protection strategies for image processing models by embedding invisible watermarks into model outputs to verify the sources of images. Most current methods rely on CNN-based architectures, which are limited by their local perception capabilities and struggle to effectively capture global information. To address this, we introduce the Swin-UNet, originally designed for medical image segmentation tasks, into the watermark embedding process. The Swin Transformer’s ability to capture global information enhances the visual quality of the embedded image compared to CNN-

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Ulyanov, Dmitry, Andrea Vedaldi, and Victor Lempitsky. "Deep Image Prior." International Journal of Computer Vision 128, no. 7 (2020): 1867–88. http://dx.doi.org/10.1007/s11263-020-01303-4.

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Malhotra, Priyanka, Sheifali Gupta, Deepika Koundal, Atef Zaguia, and Wegayehu Enbeyle. "Deep Neural Networks for Medical Image Segmentation." Journal of Healthcare Engineering 2022 (March 10, 2022): 1–15. http://dx.doi.org/10.1155/2022/9580991.

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Image segmentation is a branch of digital image processing which has numerous applications in the field of analysis of images, augmented reality, machine vision, and many more. The field of medical image analysis is growing and the segmentation of the organs, diseases, or abnormalities in medical images has become demanding. The segmentation of medical images helps in checking the growth of disease like tumour, controlling the dosage of medicine, and dosage of exposure to radiations. Medical image segmentation is really a challenging task due to the various artefacts present in the images. Rec

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Lu, Xuchao, Li Song, Rong Xie, Xiaokang Yang, and Wenjun Zhang. "Deep Binary Representation for Efficient Image Retrieval." Advances in Multimedia 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/8961091.

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With the fast growing number of images uploaded every day, efficient content-based image retrieval becomes important. Hashing method, which means representing images in binary codes and using Hamming distance to judge similarity, is widely accepted for its advantage in storage and searching speed. A good binary representation method for images is the determining factor of image retrieval. In this paper, we propose a new deep hashing method for efficient image retrieval. We propose an algorithm to calculate the target hash code which indicates the relationship between images of different conten

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Dave, Himank, Nikhil Kant, Nishank Dave, and Divya Ghorui. "BRAIN TUMOR CLASSIFICATION USING DEEP LEARNING." International Journal of Engineering Applied Sciences and Technology 6, no. 7 (2021): 227–38. http://dx.doi.org/10.33564/ijeast.2021.v06i07.037.

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The early detection of the tumor plays an important role in the recovery of the patient. In our proposed model, we have collected MRI scans as it helps with the information about the blood supply inside the brain. Thus, for the recognition of anomaly, for examining the increasing of the ailment, and for the diagnosis, we prepared a data set consisting of various MRI images. We then focused on removing unwanted noise and image enhancement. The image characteristics can be enhanced by using image preprocessing techniques. The image enhancement depends upon different factors like computational ti

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Kawano, Yasufumi, Yoshiki Nota, Rinpei Mochizuki, and Yoshimitsu Aoki. "Non-Deep Active Learning for Deep Neural Networks." Sensors 22, no. 14 (2022): 5244. http://dx.doi.org/10.3390/s22145244.

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One way to improve annotation efficiency is active learning. The goal of active learning is to select images from many unlabeled images, where labeling will improve the accuracy of the machine learning model the most. To select the most informative unlabeled images, conventional methods use deep neural networks with a large number of computation nodes and long computation time, but we propose a non-deep neural network method that does not require any additional training for unlabeled image selection. The proposed method trains a task model on labeled images, and then the model predicts unlabel

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Ksheerasagar, Deepak R. "Image Description Generator using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 4244–48. http://dx.doi.org/10.22214/ijraset.2022.45988.

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Abstract: To recognise the context of an image and describe it in a natural language like English, the fundamental task of creating image captions uses computer vision and natural language processing techniques. To create a natural language description from an input image, image caption generation is used. Convolutional Neural Network (CNN) model and Long Short-Term Memory (LSTM) model are the two parts of this Python project that are used to implement it. The CNN-LSTM architecture combines a Convolutional Neural Network (CNN), which creates features that describe the images, with a Long Short

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Amal Joseph, Binny S, Abhishek V A, Nithin Raj, and Vimel Manoj. "DEEP FACE - On the Reconstruction of Face Images from Deep Face Templates." international journal of engineering technology and management sciences 7, no. 4 (2023): 606–11. http://dx.doi.org/10.46647/ijetms.2023.v07i04.083.

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The paper on “Reconstruction of Face Images from Deep Face Templates" presents a novel approach for face image reconstruction using deep learning techniques. The proposed method utilizes a pre-trained deep face template, which is a convolutional neural network (CNN) trained on a large-scale face dataset, as a prior to guide the reconstruction process. Specifically, the method solves an optimization problem that balances the fidelity to the input image and the similarity to the deep face template. Its then evaluated with the method on two face image datasets, and demonstrate that their method o

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Kameswari, A. V. N. "Image Caption Generator Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1554–64. http://dx.doi.org/10.22214/ijraset.2021.38652.

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Abstract: When humans see an image, their brain can easily tell what the image is about, but a computer cannot do it easily. Computer vision researchers worked on this a lot and they considered it impossible until now! With the advancement in Deep learning techniques, availability of huge datasets and computer power, we can build models that can generate captions for an image. Image Caption Generator is a popular research area of Deep Learning that deals with image understanding and a language description for that image. Generating well-formed sentences requires both syntactic and semantic und

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Yang, Jaeyoung, Sooin Kim, Sangwoo Lee, Won-gyum Kim, Donghoon Kim, and Doosung Hwang. "Robust Authentication Analysis of Copyright Images through Deep Hashing Models with Self-supervision." JUCS - Journal of Universal Computer Science 29, no. 8 (2023): 938–58. http://dx.doi.org/10.3897/jucs.98824.

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The increased usage of the internet and ICT has posed a significant challenge to protect copyrighted content due to advanced image forgery techniques that make image authentication extremely difficult. The aim of this paper is to establish a binary classification method for determining copyright images from copyright-free ones. A deep hashing model is introduced for an image authentication system, which uses deep learning-based perceptual hashing. Hash codes from a deep hashing model trained with a copyright image dataset are used to identify images. The deep learning model is able to learn fe

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Yang, Jaeyoung, Sooin Kim, Sangwoo Lee, Won-gyum Kim, Donghoon Kim, and Doosung Hwang. "Robust Authentication Analysis of Copyright Images through Deep Hashing Models with Self-supervision." JUCS - Journal of Universal Computer Science 29, no. (8) (2023): 938–58. https://doi.org/10.3897/jucs.98824.

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The increased usage of the internet and ICT has posed a significant challenge to protect copyrighted content due to advanced image forgery techniques that make image authentication extremely difficult. The aim of this paper is to establish a binary classification method for determining copyright images from copyright-free ones. A deep hashing model is introduced for an image authentication system, which uses deep learning-based perceptual hashing. Hash codes from a deep hashing model trained with a copyright image dataset are used to identify images. The deep learning model is able to learn fe

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Im, Chan-Gi, Dong-Min Son, Hyuk-Ju Kwon, and Sung-Hak Lee. "Multi-Task Learning Approach Using Dynamic Hyperparameter for Multi-Exposure Fusion." Mathematics 11, no. 7 (2023): 1620. http://dx.doi.org/10.3390/math11071620.

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High-dynamic-range (HDR) image synthesis is a technology developed to accurately reproduce the actual scene of an image on a display by extending the dynamic range of an image. Multi-exposure fusion (MEF) technology, which synthesizes multiple low-dynamic-range (LDR) images to create an HDR image, has been developed in various ways including pixel-based, patch-based, and deep learning-based methods. Recently, methods to improve the synthesis quality of images using deep-learning-based algorithms have mainly been studied in the field of MEF. Despite the various advantages of deep learning, deep

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Jianwei Chen, Jianwei Chen, Quan Du Jianwei Chen, and Ling-Ju Hung Quan Du. "A Fusion Algorithm Based on Deep Learning for Panoramic Image." 電腦學刊 35, no. 6 (2024): 097–107. https://doi.org/10.53106/199115992024123506008.

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<p>Traditional image fusion algorithms often struggle with slow processing speeds and suboptimal results, particularly when handling non-planar images. In this paper, we present a novel deep learning-based approach for panoramic image fusion. We begin by detailing our dataset construction and preprocessing techniques. To enhance the model&rsquo;s capability with non-planar images, we apply the Thin Plate Spline (TPS) deformation algorithm, allowing effective panoramic fusion across complex image structures. The model architecture is based on a convolutional neural network (CNN) frame

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Hsu, Chih-Chung, Yi-Xiu Zhuang, and Chia-Yen Lee. "Deep Fake Image Detection Based on Pairwise Learning." Applied Sciences 10, no. 1 (2020): 370. http://dx.doi.org/10.3390/app10010370.

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Generative adversarial networks (GANs) can be used to generate a photo-realistic image from a low-dimension random noise. Such a synthesized (fake) image with inappropriate content can be used on social media networks, which can cause severe problems. With the aim to successfully detect fake images, an effective and efficient image forgery detector is necessary. However, conventional image forgery detectors fail to recognize fake images generated by the GAN-based generator since these images are generated and manipulated from the source image. Therefore, in this paper, we propose a deep learni

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Jacquemet, Guillaume. "Deep learning to analyse microscopy images." Biochemist 43, no. 5 (2021): 60–64. http://dx.doi.org/10.1042/bio_2021_167.

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Artificial intelligence (AI)-powered algorithms are now influencing many aspects of our day-to-day life, from providing movies/music recommendations to controlling self-driving cars. These algorithms are also increasingly used in the lab to aid biomedical research. In particular, the ability to analyse and process images using AI is slowly revolutionizing the quality and quantity of data we collect from microscopy images. In fact, AI-based algorithms can now be applied to perform virtually any high-performance image analysis tasks such as classifying images, detecting and segmenting objects, a

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Awan, Hafiz Shakeel Ahmad, and Muhammad Tariq Mahmood. "Deep Dynamic Weights for Underwater Image Restoration." Journal of Marine Science and Engineering 12, no. 7 (2024): 1208. http://dx.doi.org/10.3390/jmse12071208.

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Underwater imaging presents unique challenges, notably color distortions and reduced contrast due to light attenuation and scattering. Most underwater image enhancement methods first use linear transformations for color compensation and then enhance the image. We observed that linear transformation for color compensation is not suitable for certain images. For such images, non-linear mapping is a better choice. This paper introduces a unique underwater image restoration approach leveraging a streamlined convolutional neural network (CNN) for dynamic weight learning for linear and non-linear ma

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S, Divya. "Image Reconstruction in Surgical Field Using Deep Learning." Revista Gestão Inovação e Tecnologias 11, no. 2 (2021): 1489–96. http://dx.doi.org/10.47059/revistageintec.v11i2.1775.

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The field of medical image reconstruction helps to improve image quality by manipulating image features and artefact with Filtered-Back Propagation for X-ray Computer Tomography (CT), Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). This project focuses on detection of tumour cells using Radiomics application that aims to extract extensive quantitative features from magnetic resonance images. In this paper image discretization models and image interpolation techniques are used to segment the MR images and train them for Image Reconstruction. The image based gray level s

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Wasi, Md Adnan, Rakesh Das, Purnendu Sarkar, et al. "Image Captioning Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 521–25. http://dx.doi.org/10.22214/ijraset.2023.53625.

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Abstract: This paper focuses on developing an image captioning system using deep learning techniques. The paper aims to generate descriptive textual captions for images, enabling machines to understand and communicate the content of visual data. The methodology involves leveraging convolutional neural networks (CNNs) for image feature extraction and recurrent neural networks (RNNs) for sequential language generation. The paper includes steps such as dataset collection, data preprocessing, CNN feature extraction, RNN-based captioning model implementation, model evaluation using metrics like BLE

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Listyalina, Latifah, Yudianingsih Yudianingsih, Adjie Wibowo Soedjono, Evrita Lusiana Utari, and Dhimas Arief Dharmawan. "Deep-RIC: Plastic Waste Classification using Deep Learning and Resin Identification Codes (RIC)." Telematika 19, no. 2 (2022): 215. http://dx.doi.org/10.31315/telematika.v19i2.7419.

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In this study, the authors designed an algorithm based on deep learning that can automatically classify plastic waste according to Resin Identification Codes (RIC). The proposed algorithm is built through several stages as follows. In the first stage, image acquisition of plastic waste is carried out, which is the input of the designed algorithm. The acquired plastic waste image must display the resin code of the plastic waste to be classified. Furthermore, the acquired image is divided into two sets, namely training and testing sets. The training set contains images of plastic waste used in t

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Chyan, Phie, and Tri Saptadi. "Image Restoration Using Deep Learning Based Image Completion." Jurnal Sisfokom (Sistem Informasi dan Komputer) 12, no. 3 (2023): 335–40. http://dx.doi.org/10.32736/sisfokom.v12i3.1699.

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Digital images can experience various disturbances in acquisition and storage, one of which is a disturbance indicated by damage to certain areas of the image field and causes the loss of some of the information represented by the image. One of the ways to restore an image experiencing disturbances like this is with image completion technology. Image completion is an image restoration technology capable of filling in or completing missing or corrupted parts of an image. Various methods have been developed for this image completion, starting from those based on basic image processing to the lat

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Chen, David, Huzefa Bhopalwala, Nakeya Dewaswala, et al. "Deep Neural Network for Cardiac Magnetic Resonance Image Segmentation." Journal of Imaging 8, no. 5 (2022): 149. http://dx.doi.org/10.3390/jimaging8050149.

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The analysis and interpretation of cardiac magnetic resonance (CMR) images are often time-consuming. The automated segmentation of cardiac structures can reduce the time required for image analysis. Spatial similarities between different CMR image types were leveraged to jointly segment multiple sequences using a segmentation model termed a multi-image type UNet (MI-UNet). This model was developed from 72 exams (46% female, mean age 63 ± 11 years) performed on patients with hypertrophic cardiomyopathy. The MI-UNet for steady-state free precession (SSFP) images achieved a superior Dice similari

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Wang, Ziyang, Wei Zheng, and Youguang Chen. "Deep learning for fast bronze inscription image retrieval." Journal of Chinese Writing Systems 4, no. 4 (2020): 291–96. http://dx.doi.org/10.1177/2513850220964956.

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Collections of bronze inscription images are increasing rapidly. To use these images efficiently, we proposed an effective content-based image retrieval framework using deep learning. Specifically, we extract discriminative local features for image retrieval using the activations of the convolutional neural network and binarize the extracted features for improving the efficiency of image retrieval, firstly. Then, we use the cosine metric and Euclidean metric to calculate the similarity between the query image and dataset images. The result shows that the proposed framework has an impressive ac

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Akurathi, Aravinda, Yoshitha Challagulla, Meghana Kakarla, Sreeja Kandula, and B.Tejaswi. "Image Restoration using Deep Learning Techniques." International Journal of Engineering and Advanced Technology (IJEAT) 12, no. 5 (2022): 13–16. https://doi.org/10.35940/ijeat.E3509.0611522.

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<strong>Abstract:</strong> In the modern era, due to the emergence of various technologies, most of the human work is now being performed by the computer system. The computer’s capacity to make everything possible is increasing as by the time. Photos are used to capture or freeze the moments in one’s life. We can embrace those moments at any time by looking at the pictures. It is natural that, as time passes by, these photos gets damaged due to environmental conditions that leads to loss of our important moments. Hence, preserving the photos is as important as taking them. The proc

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Nidhi, Gajimwar Ashmi Dahiwale Isha Walde Shyamal Dhabarde Prof. Monika Walde. "Automated Image Forgery Detection With Python." International Journal of Advanced Innovative Technology in Engineering 9, no. 3 (2024): 133–38. https://doi.org/10.5281/zenodo.12516039.

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Fake image detection has become increasingly important due to the widespread use of image editing software and the proliferation of fake images on social media and other online platforms. In this project, we propose a Python-based approach for detecting fake images using deep learning techniques. Our method involves preprocessing the images, extracting relevant features using convolutional neural networks (CNNs), and training a classifier to distinguish between real and fake images. We leverage state-of-the-art deep learning frameworks such as TensorFlow or PyTorch for model development and ev

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Dr. Panguluri Vinodh Babu, Musunuri Naga Madhu, Galeeb Shaik, Kornipati Sravani, and Mohammed Nayeemur Rahman. "Fake Image Detection Using Deep Learning." international journal of engineering technology and management sciences 9, no. 2 (2025): 180–89. https://doi.org/10.46647/ijetms.2025.v09i02.025.

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Disinformation and misinformation can be spread through fake images. Fake images can be employed to influence decision-making and manipulate public opinion. Fake image detection finds,use in a number of domains including law enforcement, national security, and social media.It can also be utilized in preventing the diffusion of misinformation and disinformation. The paper recommends a deep learning approach to the detection of forged images based on transfer learning. We utilize a pre-trained CNN weights and adjust them on a set of images used to fake or not, in order to produce a system which

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Jain, Uday. "Image Captioning - A Deep Learning Approach." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 3068–72. http://dx.doi.org/10.22214/ijraset.2022.45638.

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Abstract: Image captioning is a brand-new study area in the science of computer vision. The primary goal of picture captioning is to create a natural language description for the input image. In recent years, research on natural language processing and computer vision has become increasingly interested in the problem of automatically synthesising descriptive phrases for photos. Image captioning is a crucial task that demands both the ability to create precise and accurate description phrases as well as a semantic understanding of the images. Long Short Term Memory (LSTM) is used to precisely o

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