Relevant bibliographies by topics / Deep Image Prior

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Deep Image Prior'

Author: Grafiati
Published: 28 September 2024
Last updated: 31 July 2025

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Journal articles on the topic "Deep Image Prior"

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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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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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Shi, Yu, Cien Fan, Lian Zou, Caixia Sun, and Yifeng Liu. "Unsupervised Adversarial Defense through Tandem Deep Image Priors." Electronics 9, no. 11 (2020): 1957. http://dx.doi.org/10.3390/electronics9111957.

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Deep neural networks are vulnerable to the adversarial example synthesized by adding imperceptible perturbations to the original image but can fool the classifier to provide wrong prediction outputs. This paper proposes an image restoration approach which provides a strong defense mechanism to provide robustness against adversarial attacks. We show that the unsupervised image restoration framework, deep image prior, can effectively eliminate the influence of adversarial perturbations. The proposed method uses multiple deep image prior networks called tandem deep image priors to recover the ori
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Gong, Kuang, Ciprian Catana, Jinyi Qi, and Quanzheng Li. "PET Image Reconstruction Using Deep Image Prior." IEEE Transactions on Medical Imaging 38, no. 7 (2019): 1655–65. http://dx.doi.org/10.1109/tmi.2018.2888491.

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Feng, Berthy T., Katherine L. Bouman, and William T. Freeman. "Event-horizon-scale Imaging of M87* under Different Assumptions via Deep Generative Image Priors." Astrophysical Journal 975, no. 2 (2024): 201. http://dx.doi.org/10.3847/1538-4357/ad737f.

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Abstract Reconstructing images from the Event Horizon Telescope (EHT) observations of M87*, the supermassive black hole at the center of the galaxy M87, depends on a prior to impose desired image statistics. However, given the impossibility of directly observing black holes, there is no clear choice for a prior. We present a framework for flexibly designing a range of priors, each bringing different biases to the image reconstruction. These priors can be weak (e.g., impose only basic natural-image statistics) or strong (e.g., impose assumptions of black hole structure). Our framework uses Baye
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Han, Sujy, Tae Bok Lee, and Yong Seok Heo. "Deep Image Prior for Super Resolution of Noisy Image." Electronics 10, no. 16 (2021): 2014. http://dx.doi.org/10.3390/electronics10162014.

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Single image super-resolution task aims to reconstruct a high-resolution image from a low-resolution image. Recently, it has been shown that by using deep image prior (DIP), a single neural network is sufficient to capture low-level image statistics using only a single image without data-driven training such that it can be used for various image restoration problems. However, super-resolution tasks are difficult to perform with DIP when the target image is noisy. The super-resolved image becomes noisy because the reconstruction loss of DIP does not consider the noise in the target image. Furth
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Xie, Zhonghua, Lingjun Liu, Zhongliang Luo, and Jianfeng Huang. "Image Denoising Using Nonlocal Regularized Deep Image Prior." Symmetry 13, no. 11 (2021): 2114. http://dx.doi.org/10.3390/sym13112114.

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Deep neural networks have shown great potential in various low-level vision tasks, leading to several state-of-the-art image denoising techniques. Training a deep neural network in a supervised fashion usually requires the collection of a great number of examples and the consumption of a significant amount of time. However, the collection of training samples is very difficult for some application scenarios, such as the full-sampled data of magnetic resonance imaging and the data of satellite remote sensing imaging. In this paper, we overcome the problem of a lack of training data by using an u
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Chen, Yingxia, Yuqi Li, Tingting Wang, Yan Chen, and Faming Fang. "DPDU-Net: Double Prior Deep Unrolling Network for Pansharpening." Remote Sensing 16, no. 12 (2024): 2141. http://dx.doi.org/10.3390/rs16122141.

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The objective of the pansharpening task is to integrate multispectral (MS) images with low spatial resolution (LR) and to integrate panchromatic (PAN) images with high spatial resolution (HR) to generate HRMS images. Recently, deep learning-based pansharpening methods have been widely studied. However, traditional deep learning methods lack transparency while deep unrolling methods have limited performance when using one implicit prior for HRMS images. To address this issue, we incorporate one implicit prior with a semi-implicit prior and propose a double prior deep unrolling network (DPDU-Net
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You, Shaopei, Jianlou Xu, Yajing Fan, Yuying Guo, and Xiaodong Wang. "Combining Deep Image Prior and Second-Order Generalized Total Variance for Image Inpainting." Mathematics 11, no. 14 (2023): 3201. http://dx.doi.org/10.3390/math11143201.

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Image inpainting is a crucial task in computer vision that aims to restore missing and occluded parts of damaged images. Deep-learning-based image inpainting methods have gained popularity in recent research. One such method is the deep image prior, which is unsupervised and does not require a large number of training samples. However, the deep image prior method often encounters overfitting problems, resulting in blurred image edges. In contrast, the second-order total generalized variation can effectively protect the image edge information. In this paper, we propose a novel image restoration
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Dissertations / Theses on the topic "Deep Image Prior"

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Liu, Yang. "Application of prior information to discriminative feature learning." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/285558.

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Learning discriminative feature representations has attracted a great deal of attention since it is a critical step to facilitate the subsequent classification, retrieval and recommendation tasks. In this dissertation, besides incorporating prior knowledge about image labels into the image classification as most prevalent feature learning methods currently do, we also explore some other general-purpose priors and verify their effectiveness in the discriminant feature learning. As a more powerful representation can be learned by implementing such general priors, our approaches achieve state-of-
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Merasli, Alexandre. "Reconstruction d’images TEP par des méthodes d’optimisation hybrides utilisant un réseau de neurones non supervisé et de l'information anatomique." Electronic Thesis or Diss., Nantes Université, 2024. http://www.theses.fr/2024NANU1003.

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La TEP est une modalité d’imagerie fonctionnelle utilisée en oncologie permettant de réaliser une imagerie quantitative de la distribution d’un traceur radioactif injecté au patient. Les données brutes TEP présentent un niveau de bruit intrinsèquement élevé et une résolution spatiale modeste, en comparaison avec les modalités d’imagerie anatomiques telles que l’IRM et la TDM. Par ailleurs, les méthodes standards de reconstruction des images TEP à partir des données brutes introduisent du biais positif dans les régions de faible activité, en particulier dans le cas de faibles statistiques d'acq
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Deng, Mo Ph D. Massachusetts Institute of Technology. "Deep learning with physical and power-spectral priors for robust image inversion." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127013.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020<br>Cataloged from the official PDF of thesis.<br>Includes bibliographical references (pages 169-182).<br>Computational imaging is the class of imaging systems that utilizes inverse algorithms to recover unknown objects of interest from physical measurements. Deep learning has been used in computational imaging, typically in the supervised mode and in an End-to-End fashion. However, treating the machine learning algorithm as a mere black-box is not the most efficient, as t
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Ganaye, Pierre-Antoine. "A priori et apprentissage profond pour la segmentation en imagerie cérébrale." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI100.

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L'imagerie médicale est un domaine vaste guidé par les avancées en instrumentation, en techniques d'acquisition et en traitement d’images. Les progrès réalisés dans ces grandes disciplines concourent tous à l'amélioration de la compréhension de phénomènes physiologiques comme pathologiques. En parallèle, l'accès à des bases de données d'imagerie plus large, associé au développement de la puissance de calcul, a favorisé le développement de méthodologies par apprentissage machine pour le traitement automatique des images dont les approches basées sur des réseaux de neurones profonds. Parmi les a
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Zheng-YiLi and 李政毅. "Structural RPN: Integrating Prior Parametric Model to Deep CNN for Medical Image Applications." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/326476.

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Pandey, Gaurav. "Deep Learning with Minimal Supervision." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4315.

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Abstract In recent years, deep neural networks have achieved extraordinary performance on supervised learning tasks. Convolutional neural networks (CNN) have vastly improved the state of the art for most computer vision tasks including object recognition and segmentation. However, their success relies on the presence of a large amount of labeled data. In contrast, relatively fewer work has been done in deep learning to handle scenarios when access to ground truth is limited, partial or completely absent. In this thesis, we propose models to handle challenging problems with limited labeled inf
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Book chapters on the topic "Deep Image Prior"

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Wang, Hongyan, Xin Wang, and Zhixun Su. "Single Image Dehazing with Deep-Image-Prior Networks." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46311-2_7.

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Dittmer, Sören, Tobias Kluth, Daniel Otero Baguer, and Peter Maass. "A Deep Prior Approach to Magnetic Particle Imaging." In Machine Learning for Medical Image Reconstruction. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61598-7_11.

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Laves, Max-Heinrich, Malte Tölle, and Tobias Ortmaier. "Uncertainty Estimation in Medical Image Denoising with Bayesian Deep Image Prior." In Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60365-6_9.

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Benfenati, Alessandro, Ambra Catozzi, Giorgia Franchini, and Federica Porta. "Piece-wise Constant Image Segmentation with a Deep Image Prior Approach." In Lecture Notes in Computer Science. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31975-4_27.

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Agazzotti, Gaetano, Fabien Pierre, and Frédéric Sur. "Deep Image Prior Regularized by Coupled Total Variation for Image Colorization." In Lecture Notes in Computer Science. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-31975-4_23.

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Ferreira, Leonardo A., Roberto G. Beraldo, Ricardo Suyama, Fernando S. Moura, and André K. Takahata. "2D Electrical Impedance Tomography Brain Image Reconstruction Using Deep Image Prior." In IFMBE Proceedings. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-49404-8_27.

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Sudarshan, Viswanath P., K. Pavan Kumar Reddy, Mohana Singh, Jayavardhana Gubbi, and Arpan Pal. "Uncertainty-Informed Bayesian PET Image Reconstruction Using a Deep Image Prior." In Machine Learning for Medical Image Reconstruction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17247-2_15.

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Meyer, Lina, Lena-Marie Woelk, Christine E. Gee, et al. "Deep Image Prior for Spatio-temporal Fluorescence Microscopy Images DECO-DIP." In Bildverarbeitung für die Medizin 2024. Springer Fachmedien Wiesbaden, 2024. http://dx.doi.org/10.1007/978-3-658-44037-4_82.

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Chen, Yun-Chun, Chen Gao, Esther Robb, and Jia-Bin Huang. "NAS-DIP: Learning Deep Image Prior with Neural Architecture Search." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58523-5_26.

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Pan, Xingang, Xiaohang Zhan, Bo Dai, Dahua Lin, Chen Change Loy, and Ping Luo. "Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58536-5_16.

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Conference papers on the topic "Deep Image Prior"

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Shabtay, Nimrod, Eli Schwartz, and Raja Giryes. "Deep Phase Coded Image Prior." In 2024 IEEE International Conference on Computational Photography (ICCP). IEEE, 2024. http://dx.doi.org/10.1109/iccp61108.2024.10645026.

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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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Gao, Yaru, Yanxiang Hu, Bo Zhang, Caixia Hao, and Xinran Chen. "Unsupervised Multi Focus Image Fusion based on Deep Image Prior." In 2024 IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). IEEE, 2024. https://doi.org/10.1109/iciba62489.2024.10868254.

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Liang, Shijun, Ismail Alkhouri, Qing Qu, Rongrong Wang, and Saiprasad Ravishankar. "Sequential Diffusion-Guided Deep Image Prior for Medical Image Reconstruction." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10890153.

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Jiang, Min, Qingtao Sun, and Qing Huo Liu. "Deep Image Prior Enabled Two-Dimensional Electromagnetic Computation." In 2024 International Applied Computational Electromagnetics Society Symposium (ACES-China). IEEE, 2024. http://dx.doi.org/10.1109/aces-china62474.2024.10699502.

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Hashimoto, F., K. Ote, H. Tashima, et al. "An Approach to Reliable PET Image Denoising Using Deep Image Prior." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10657678.

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Hori, K., and T. Hashimoto. "Direct image reconstruction using deep image prior in limited-angle SPECT." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10658412.

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Zhao, Z., H. Zou, H. Liu, D. Liang, S. Xie, and Q. Peng. "Dynamic PET Image Reconstruction Using Spatiotemporal Kernel Method With Deep Image Prior." In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD). IEEE, 2024. http://dx.doi.org/10.1109/nss/mic/rtsd57108.2024.10657386.

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Zhang, Yifan, Chaoqun Dong, and Shaohui Mei. "Cycle-Consistent Sparse Unmixing Network Based on Deep Image Prior." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10641125.

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Sultan, Muhammad Ahmad, Chong Chen, Yingmin Liu, Xuan Lei, and Rizwan Ahmad. "Deep Image Prior with Structured Sparsity (Discus) for Dynamic MRI Reconstruction." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635579.

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