Relevant bibliographies by topics / Deep image

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Deep image'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Deep image"

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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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Dissertations / Theses on the topic "Deep image"

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Cabrera, Gil Blanca. "Deep Learning Based Deformable Image Registration of Pelvic Images." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279155.

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Deformable image registration is usually performed manually by clinicians,which is time-consuming and costly, or using optimization-based algorithms, which are not always optimal for registering images of different modalities. In this work, a deep learning-based method for MR-CT deformable image registration is presented. In the first place, a neural network is optimized to register CT pelvic image pairs. Later, the model is trained on MR-CT image pairs to register CT images to match its MR counterpart. To solve the unavailability of ground truth data problem, two approaches were used. For the
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Ma, Sihan. "Image Matting via Deep Learning." Thesis, The University of Sydney, 2020. https://hdl.handle.net/2123/22426.

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Image matting aims to extract the accurate opacity of the foreground from the input RGB image, which is beneficial and essential for the subsequent applications, such as image editing, compositing, and film production. Unfortunately, this task is challenging because of its ill-posed nature. Specifically, with the corresponding foreground and background unknown, it is hard to predict the opacity of the foreground from the single RGB image. Recently, deep learning is introduced to image matting to deal with this problem. However, there are still some issues to be addressed. First, the choice of
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Dumas, Thierry. "Deep learning for image compression." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S029/document.

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Ces vingt dernières années, la quantité d’images et de vidéos transmises a augmenté significativement, ce qui est principalement lié à l’essor de Facebook et Netflix. Même si les capacités de transmission s’améliorent, ce nombre croissant d’images et de vidéos transmises exige des méthodes de compression plus efficaces. Cette thèse a pour but d’améliorer par l’apprentissage deux composants clés des standards modernes de compression d’image, à savoir la transformée et la prédiction intra. Plus précisément, des réseaux de neurones profonds sont employés car ils ont un grand pouvoir d’approximati
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Siarohin, Aliaksandr. "Image Animation Using Deep Learning." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/310291.

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Recently media content generation, particularly image and video, using deep learning gained a lot of attention in the research community. One of the main reasons for that is the surge of the interactions in the social networks, that draw a lot of people without specialized backgrounds into the media industry. This raises the interest in the tolls for simplifying the production of the media content, such as images and videos. Another potential avenue for deep learning methods is a simplification of the content generation for the traditional media, especially creation of movies, visual effects f
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Siarohin, Aliaksandr. "Image Animation Using Deep Learning." Doctoral thesis, Università degli studi di Trento, 2021. http://hdl.handle.net/11572/310291.

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Recently media content generation, particularly image and video, using deep learning gained a lot of attention in the research community. One of the main reasons for that is the surge of the interactions in the social networks, that draw a lot of people without specialized backgrounds into the media industry. This raises the interest in the tolls for simplifying the production of the media content, such as images and videos. Another potential avenue for deep learning methods is a simplification of the content generation for the traditional media, especially creation of movies, visual effects f
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Zhang, Edwin Meng. "Image Miner : an architecture to support deep mining of images." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/100612.

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Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 69-70).<br>In this thesis, I designed a cloud based system, called ImageMiner, to tune parameters of feature extraction process in a machine learning pipeline for images. Feature extractio
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Dunlop, J. S., R. J. McLure, A. D. Biggs, et al. "A deep ALMA image of the Hubble Ultra Deep Field." OXFORD UNIV PRESS, 2017. http://hdl.handle.net/10150/623849.

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We present the results of the first, deep Atacama Large Millimeter Array ( ALMA) imaging covering the full similar or equal to 4.5 arcmin(2) of the Hubble Ultra Deep Field ( HUDF) imaged with Wide Field Camera 3/IR on HST. Using a 45-pointing mosaic, we have obtained a homogeneous 1.3-mm image reaching sigma 1.3 similar or equal to 35 mu Jy, at a resolution of similar or equal to 0.7 arcsec. From an initial list of similar or equal to 50 > 3.5 sigma peaks, a rigorous analysis confirms 16 sources with S-1.3 > 120 mu Jy. All of these have secure galaxy counterparts with robust redshifts (< z > =
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Zeledon, Lostalo Emilia Maria. "FMRI IMAGE REGISTRATION USING DEEP LEARNING." OpenSIUC, 2019. https://opensiuc.lib.siu.edu/theses/2641.

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fMRI imaging is considered key on the understanding of the brain and the mind, for this reason has been the subject of tremendous research connecting different disciplines. The intrinsic complexity of this 4-D type of data processing and analysis has been approached with every single computational perspective, lately increasing the trend to include artificial intelligence. One step critical on the fMRI pipeline is image registration. A model of Deep Networks based on Fully Convolutional Neural Networks, spatial transformation neural networks with a self-learning strategy was proposed for the i
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Hossain, Md Zakir. "Deep learning techniques for image captioning." Thesis, Hossain, Md. Zakir (2020) Deep learning techniques for image captioning. PhD thesis, Murdoch University, 2020. https://researchrepository.murdoch.edu.au/id/eprint/60782/.

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Generating a description of an image is called image captioning. Image captioning is a challenging task because it involves the understanding of the main objects, their attributes, and their relationships in an image. It also involves the generation of syntactically and semantically meaningful descriptions of the images in natural language. A typical image captioning pipeline comprises an image encoder and a language decoder. Convolutional Neural Networks (CNNs) are widely used as the encoder while Long short-term memory (LSTM) networks are used as the decoder. A variety of LSTMs and CNNs incl
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Tensmeyer, Christopher Alan. "Deep Learning for Document Image Analysis." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7389.

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Automatic machine understanding of documents from image inputs enables many applications in modern document workflows, digital archives of historical documents, and general machine intelligence, among others. Together, the techniques for understanding document images comprise the field of Document Image Analysis (DIA). Within DIA, the research community has identified several sub-problems, such as page segmentation and Optical Character Recognition (OCR). As the field has matured, there has been a trend of moving away from heuristic-based methods, designed for particular tasks and domains of
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Books on the topic "Deep image"

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Lee, Gobert, and Hiroshi Fujita, eds. Deep Learning in Medical Image Analysis. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33128-3.

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Indrakumari, R., T. Ganesh Kumar, D. Murugan, and Sherimon P.C. Deep Learning in Medical Image Analysis. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003343172.

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Roy, Sanjiban Sekhar, Ching-Hsien Hsu, and Venkateshwara Kagita, eds. Deep Learning Applications in Image Analysis. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3784-4.

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Mire, Archana, Vinayak Elangovan, and Shailaja Patil. Advances in Deep Learning for Medical Image Analysis. CRC Press, 2022. http://dx.doi.org/10.1201/9781003230540.

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Tao, Linmi, and Atif Mughees. Deep Learning for Hyperspectral Image Analysis and Classification. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4420-4.

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Lu, Le, Yefeng Zheng, Gustavo Carneiro, and Lin Yang, eds. Deep Learning and Convolutional Neural Networks for Medical Image Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-42999-1.

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Soufiene, Ben Othman, and Chinmay Chakraborty. Machine Learning and Deep Learning Techniques for Medical Image Recognition. CRC Press, 2023. http://dx.doi.org/10.1201/9781003366249.

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Chaki, Jyotismita. The Art of Deep Learning Image Augmentation: The Seeds of Success. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-5081-1.

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Ganem, Gabriel Loaiza. Advances in Deep Generative Modeling With Applications to Image Generation and Neuroscience. [publisher not identified], 2019.

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Haltmeier, Markus, Johannes Schwab, and Stephan Antholzer. Deep Learning for Image Reconstruction. World Scientific Publishing Co Pte Ltd, 2020.

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Book chapters on the topic "Deep image"

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Berg, Charles. "The Mother-Image." In Deep Analysis. Routledge, 2021. http://dx.doi.org/10.4324/9781003251552-17.

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Kampffmeyer, Michael, Sigurd Løkse, Filippo M. Bianchi, Robert Jenssen, and Lorenzo Livi. "Deep Kernelized Autoencoders." In Image Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59126-1_35.

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Huang, Yanhua. "Robust Image Enhancement." In Deep Reinforcement Learning. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-4095-0_14.

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Paluszek, Michael, and Stephanie Thomas. "Image Classification." In Practical MATLAB Deep Learning. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5124-9_11.

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Paluszek, Michael, Stephanie Thomas, and Eric Ham. "Image Classification." In Practical MATLAB Deep Learning. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7912-0_11.

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Trullo, Roger, Quoc-Anh Bui, Qi Tang, and Reza Olfati-Saber. "Image Translation Based Nuclei Segmentation for Immunohistochemistry Images." In Deep Generative Models. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18576-2_9.

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Zhu, Song-Chun, and Ying Wu. "Deep Image Models." In Computer Vision. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96530-3_11.

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Nordeng, Ian E., Ahmad Hasan, Doug Olsen, and Jeremiah Neubert. "DEBC Detection with Deep Learning." In Image Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59126-1_21.

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Lin, John, Mohamed El Amine Seddik, Mohamed Tamaazousti, Youssef Tamaazousti, and Adrien Bartoli. "Deep Multi-class Adversarial Specularity Removal." In Image Analysis. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20205-7_1.

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Banerjee, Subhashis, and Robin Strand. "Deep Active Learning for Glioblastoma Quantification." In Image Analysis. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-31435-3_13.

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Conference papers on the topic "Deep image"

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Zhu, Qiang, Kuan Lu, Menghao Huo, and Yuxiao Li. "Image-to-Image Translation with Diffusion Transformers and CLIP-Based Image Conditioning." In 2025 6th International Conference on Computer Vision, Image and Deep Learning (CVIDL). IEEE, 2025. https://doi.org/10.1109/cvidl65390.2025.11085477.

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Yuan, Weimin, Yinuo Wang, Ning Li, Cai Meng, and Xiangzhi Bai. "Mixed Degradation Image Restoration via Deep Image Prior Empowered by Deep Denoising Engine." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650215.

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Faghihpirayesh, Razieh, Xueqi Guo, Matthias M. Wolf, Kaman Chung, and Mohammad Abdi. "Deep-learning framework for analysis of longitudinal MRI studies." In Image Processing, edited by Olivier Colliot and Jhimli Mitra. SPIE, 2025. https://doi.org/10.1117/12.3047794.

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Cho, Soojin, and Byunghyun Kim. "Image-driven Bridge Inspection Framework using Deep Learning and Image Registration." In IABSE Conference, Seoul 2020: Risk Intelligence of Infrastructures. International Association for Bridge and Structural Engineering (IABSE), 2020. http://dx.doi.org/10.2749/seoul.2020.269.

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&lt;p&gt;This paper proposes an image-driven bridge inspection framework using automated damage detection using deep learning technique and image registration. A state-of-the-art deep learning model, Cascade Mask R-CNN (Mask and Region-based Convolutional Neural Networks) is trained for detection of cracks, which is a representative damage type of bridges, from the images taken from a bridge. The model is trained with more than a thousand training images containing cracks as well as crack-like objects (hard negative samples). The images taken from a test bridge are input to a deep learning mod
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Wijethilake, Navodini, Mithunjha Anandakumar, Cheng Zheng, Peter T. C. So, Murat Yildirim, and Dushan N. Wadduwage. "DEEP2: Deep Learning Powered De-scattering with Excitation Patterning (DEEP)." In Optics and the Brain. Optica Publishing Group, 2023. http://dx.doi.org/10.1364/brain.2023.bw3b.3.

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We present DEEP2, a computational multiphoton microscope to image through scattering tissue. In DEEP2, temporally focused structured light excites deep tissue in wide-field, and deep learning reconstructs clean images from scattered measurements.
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Li, Jizhizi, Jing Zhang, and Dacheng Tao. "Deep Automatic Natural Image Matting." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/111.

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Automatic image matting (AIM) refers to estimating the soft foreground from an arbitrary natural image without any auxiliary input like trimap, which is useful for image editing. Prior methods try to learn semantic features to aid the matting process while being limited to images with salient opaque foregrounds such as humans and animals. In this paper, we investigate the difficulties when extending them to natural images with salient transparent/meticulous foregrounds or non-salient foregrounds. To address the problem, a novel end-to-end matting network is proposed, which can predict a genera
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Zhao, Zixiang, Shuang Xu, Chunxia Zhang, Junmin Liu, Jiangshe Zhang, and Pengfei Li. "DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/135.

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Infrared and visible image fusion, a hot topic in the field of image processing, aims at obtaining fused images keeping the advantages of source images. This paper proposes a novel auto-encoder (AE) based fusion network. The core idea is that the encoder decomposes an image into background and detail feature maps with low- and high-frequency information, respectively, and that the decoder recovers the original image. To this end, the loss function makes the background/detail feature maps of source images similar/dissimilar. In the test phase, background and detail feature maps are respectively
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Wang, Yu, Yi Niu, Peiyong Duan, Jianwei Lin, and Yuanjie Zheng. "Deep Propagation Based Image Matting." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/139.

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In this paper, we propose a deep propagation based image matting framework by introducing deep learning into learning an alpha matte propagation principal. Our deep learning architecture is a concatenation of a deep feature extraction module, an affinity learning module and a matte propagation module. These three modules are all differentiable and can be optimized jointly via an end-to-end training process. Our framework results in a semantic-level pairwise similarity of pixels for propagation by learning deep image representations adapted to matte propagation. It combines the power of deep le
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Tsai, Yi-Hsuan, Xiaohui Shen, Zhe Lin, Kalyan Sunkavalli, Xin Lu, and Ming-Hsuan Yang. "Deep Image Harmonization." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.299.

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Xu, Ning, Brian Price, Scott Cohen, and Thomas Huang. "Deep Image Matting." In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2017. http://dx.doi.org/10.1109/cvpr.2017.41.

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Reports on the topic "Deep image"

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George, Bennie. Histological Image Analysis: A Deep Dive. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.kk5ytyrh.

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Wachs, Brandon. Satellite Image Deep Fake Creation and Detection. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1812627.

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Cui, Yonggang. Using Deep Learning Algorithm to Enhance Image-review Software for Surveillance Cameras. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1477475.

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Cui, Y. Using Deep Learning Algorithm to Enhance Image-review Software for Surveillance Cameras. Office of Scientific and Technical Information (OSTI), 2017. http://dx.doi.org/10.2172/1413952.

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Cui, Yonggang, and Maikael A. Thomas. Using Deep Learning Algorithm to Enhance Image-review Software for Surveillance Cameras. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1436246.

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Cui, Yonggang. Using Deep Learning Algorithm to Enhance Image-review Software for Surveillance Cameras. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1524538.

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Shu, Mengying. Deep learning for image classification on very small datasets using transfer learning. Iowa State University, 2019. http://dx.doi.org/10.31274/cc-20240624-493.

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Varastehpour, Soheil, Hamid Sharifzadeh, and Iman Ardekani. A Comprehensive Review of Deep Learning Algorithms. Unitec ePress, 2021. http://dx.doi.org/10.34074/ocds.092.

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Deep learning algorithms are a subset of machine learning algorithms that aim to explore several levels of the distributed representations from the input data. Recently, many deep learning algorithms have been proposed to solve traditional artificial intelligence problems. In this review paper, some of the up-to-date algorithms of this topic in the field of computer vision and image processing are reviewed. Following this, a brief overview of several different deep learning methods and their recent developments are discussed.
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Meni, Mackenzie, Ryan White, Michael Mayo, and Kevin Pilkiewicz. Entropy-based guidance of deep neural networks for accelerated convergence and improved performance. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49805.

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Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building and training them are not straightforward processes. To add structure to these efforts, we derive new mathematical results to efficiently measure the changes in entropy as fully-connected and convolutional neural networks process data. By measuring the change in entropy as networks process data effectively, patterns critical to a well-performing network can
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Mbani, Benson, Timm Schoening, and Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, 2023. http://dx.doi.org/10.3289/sw_2_2023.

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The Automated and Integrated Seafloor Classification Workflow (AI-SCW) is a semi-automated underwater image processing pipeline that has been customized for use in classifying the seafloor into semantic habitat categories. The current implementation has been tested against a sequence of underwater images collected by the Ocean Floor Observation System (OFOS), in the Clarion-Clipperton Zone of the Pacific Ocean. Despite this, the workflow could also be applied to images acquired by other platforms such as an Autonomous Underwater Vehicle (AUV), or Remotely Operated Vehicle (ROV). The modules in
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