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Artykuły w czasopismach na temat „Deep Image Prior”

Autor: Grafiati

Data publikacji: 28 września 2024

Data aktualizacji: 31 lipca 2025

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1

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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7

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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8

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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9

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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10

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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11

Dittmer, Sören, Tobias Kluth, Peter Maass, and Baguer Daniel Otero. "Regularization by Architecture: A Deep Prior Approach for Inverse Problems." Journal of Mathematical Imaging and Vision 62 (October 30, 2019): 456–70. https://doi.org/10.1007/s10851-019-00923-x.

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The present paper studies so-called deep image prior (DIP) techniques in the context of ill-posed inverse problems. DIP networks have been recently introduced for applications in image processing; also first experimental results for applyingDIP to inverse problems have been reported. This paper aims at discussing different interpretations of DIP and to obtain analytic results for specific network designs and linear operators. The main contribution is to introduce the idea of viewing these approaches as the optimization of Tikhonov functionals rather than optimizing networks. Besides theoretica

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12

Fan, Wenshi, Hancheng Yu, Tianming Chen, and Sheng Ji. "OCT Image Restoration Using Non-Local Deep Image Prior." Electronics 9, no. 5 (2020): 784. http://dx.doi.org/10.3390/electronics9050784.

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In recent years, convolutional neural networks (CNN) have been widely used in image denoising for their high performance. One difficulty in applying the CNN to medical image denoising such as speckle reduction in the optical coherence tomography (OCT) image is that a large amount of high-quality data is required for training, which is an inherent limitation for OCT despeckling. Recently, deep image prior (DIP) networks have been proposed for image restoration without pre-training since the CNN structures have the intrinsic ability to capture the low-level statistics of a single image. However,

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13

李, 都. "Multiplicative Noise Image Denoising Based on Deep Image Prior." Advances in Applied Mathematics 12, no. 05 (2023): 2227–34. http://dx.doi.org/10.12677/aam.2023.125228.

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14

Wu, Yumo, Jianing Sun, Wengu Chen, and Junping Yin. "Improved Image Compressive Sensing Recovery with Low-Rank Prior and Deep Image Prior." Signal Processing 205 (April 2023): 108896. http://dx.doi.org/10.1016/j.sigpro.2022.108896.

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15

Hu, Yong, Shaoping Xu, Xiaohui Cheng, Changfei Zhou, and Yufeng Hu. "A Triple Deep Image Prior Model for Image Denoising Based on Mixed Priors and Noise Learning." Applied Sciences 13, no. 9 (2023): 5265. http://dx.doi.org/10.3390/app13095265.

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Image denoising poses a significant challenge in computer vision due to the high-level visual task’s dependency on image quality. Several advanced denoising models have been proposed in recent decades. Recently, deep image prior (DIP), using a particular network structure and a noisy image to achieve denoising, has provided a novel image denoising method. However, the denoising performance of the DIP model still lags behind that of mainstream denoising models. To improve the performance of the DIP denoising model, we propose a TripleDIP model with internal and external mixed images priors for

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16

Feng, Yayuan, Yu Shi, and Dianjun Sun. "Blind Poissonian Image Deblurring Regularized by a Denoiser Constraint and Deep Image Prior." Mathematical Problems in Engineering 2020 (August 24, 2020): 1–15. http://dx.doi.org/10.1155/2020/9483521.

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The denoising and deblurring of Poisson images are opposite inverse problems. Single image deblurring methods are sensitive to image noise. A single noise filter can effectively remove noise in advance, but it also damages blurred information. To simultaneously solve the denoising and deblurring of Poissonian images better, we learn the implicit deep image prior from a single degraded image and use the denoiser as a regularization term to constrain the latent clear image. Combined with the explicit L0 regularization prior of the image, the denoising and deblurring model of the Poisson image is

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17

Guo, Yanjun, Guimin Jia, and Pengyu Lu. "IRDNet: An image-defogging algorithm based on dark channel prior." Journal of Physics: Conference Series 2858, no. 1 (2024): 012042. http://dx.doi.org/10.1088/1742-6596/2858/1/012042.

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Abstract Image-defogging, as an important part of computer vision, has been widely used in intelligent driving, target recognition, satellite detection, underwater exploration and so on. Improving the performance of the defogging algorithm based on deep learning has practical significance for the completion of high-level vision tasks. This paper proposes an IRDNet algorithm by improving the image deep learning algorithm based on the RefineDNet framework and designing a new deep learning network structure. The proposed algorithm combines dark channel prior knowledge and atmospheric degradation

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18

Lin, Huangxing, Yihong Zhuang, Xinghao Ding, et al. "Self-Supervised Image Denoising Using Implicit Deep Denoiser Prior." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 1586–94. http://dx.doi.org/10.1609/aaai.v37i2.25245.

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We devise a new regularization for denoising with self-supervised learning. The regularization uses a deep image prior learned by the network, rather than a traditional predefined prior. Specifically, we treat the output of the network as a ``prior'' that we again denoise after ``re-noising.'' The network is updated to minimize the discrepancy between the twice-denoised image and its prior. We demonstrate that this regularization enables the network to learn to denoise even if it has not seen any clean images. The effectiveness of our method is based on the fact that CNNs naturally tend to cap

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19

A, Senthil Anandhi, and M. Jaiganesh. "A Synergistic Approach to Image Restoration DenseNet Enhanced Deep Image Prior." E3S Web of Conferences 616 (2025): 02026. https://doi.org/10.1051/e3sconf/202561602026.

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The DenseNet-Enhanced Deep Image Prior (Dense-DIP) model employs a combination of theories from DenseNet architecture and Deep Image Prior framework to achieve the best results in terms of image restoration. Such a cutting-edge method utilizes the densely connected layers of DenseNet for efficient recycling of features around the boned edges thus ensuring effective extraction of hierarchical features without compromising the finer details in the structures. The subtraction of Dense-DIP from pre-trained networks and large datasets for labeling is possible as the method begins with a random nois

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20

Xu, Lu, and Ying Wei. "“Pyramid Deep dehazing”: An unsupervised single image dehazing method using deep image prior." Optics & Laser Technology 148 (April 2022): 107788. http://dx.doi.org/10.1016/j.optlastec.2021.107788.

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Ho, Kary, Andrew Gilbert, Hailin Jin, and John Collomosse. "Neural architecture search for deep image prior." Computers & Graphics 98 (August 2021): 188–96. http://dx.doi.org/10.1016/j.cag.2021.05.013.

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22

Zhou, Kevin C., and Roarke Horstmeyer. "Diffraction tomography with a deep image prior." Optics Express 28, no. 9 (2020): 12872. http://dx.doi.org/10.1364/oe.379200.

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23

Fang, Yingying, and Tieyong Zeng. "Learning deep edge prior for image denoising." Computer Vision and Image Understanding 200 (November 2020): 103044. http://dx.doi.org/10.1016/j.cviu.2020.103044.

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Gao, Xianjie, Mingliang Zhang, and Jinming Luo. "Low-Light Image Enhancement via Retinex-Style Decomposition of Denoised Deep Image Prior." Sensors 22, no. 15 (2022): 5593. http://dx.doi.org/10.3390/s22155593.

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Low-light images are a common phenomenon when taking photos in low-light environments with inappropriate camera equipment, leading to shortcomings such as low contrast, color distortion, uneven brightness, and high loss of detail. These shortcomings are not only subjectively annoying but also affect the performance of many computer vision systems. Enhanced low-light images can be better applied to image recognition, object detection and image segmentation. This paper proposes a novel RetinexDIP method to enhance images. Noise is considered as a factor in image decomposition using deep learning

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25

Yamawaki, Kazuhiro, and Xian-Hua Han. "Zero-Shot Blind Learning for Single-Image Super-Resolution." Information 14, no. 1 (2023): 33. http://dx.doi.org/10.3390/info14010033.

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Deep convolutional neural networks (DCNNs) have manifested significant performance gains for single-image super-resolution (SISR) in the past few years. Most of the existing methods are generally implemented in a fully supervised way using large-scale training samples and only learn the SR models restricted to specific data. Thus, the adaptation of these models to real low-resolution (LR) images captured under uncontrolled imaging conditions usually leads to poor SR results. This study proposes a zero-shot blind SR framework via leveraging the power of deep learning, but without the requiremen

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Park, Yunjin, Sukho Lee, Byeongseon Jeong, and Jungho Yoon. "Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network." Sensors 20, no. 10 (2020): 2970. http://dx.doi.org/10.3390/s20102970.

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A joint demosaicing and denoising task refers to the task of simultaneously reconstructing and denoising a color image from a patterned image obtained by a monochrome image sensor with a color filter array. Recently, inspired by the success of deep learning in many image processing tasks, there has been research to apply convolutional neural networks (CNNs) to the task of joint demosaicing and denoising. However, such CNNs need many training data to be trained, and work well only for patterned images which have the same amount of noise they have been trained on. In this paper, we propose a var

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27

Du, Wanlin. "An investigation of rain streak removal models based on expert experience and deep learning." Highlights in Science, Engineering and Technology 56 (July 14, 2023): 14–28. http://dx.doi.org/10.54097/hset.v56i.9812.

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Computer vision technology has a wide range of applications in today's society, and image rain removal is of great importance in outdoor vision capture. Today's image de-rain techniques are divided into video de-rain, and image de-rain, with the image de-rain task being more difficult than the video de-rain task due to the lack of a time factor. Current image rain removal methods are divided into three main types: filter-based methods, a priori knowledge-based methods and deep learning methods. Although these methods can achieve the image rain removal requirements to a certain extent, there is

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Shu, Ziyu, and Zhixin Pan. "SDIP: Self-reinforcement deep image prior framework for image processing." Pattern Recognition 168 (December 2025): 111786. https://doi.org/10.1016/j.patcog.2025.111786.

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Zhu, Xuanyu, Yang Gao, Zhuang Xiong, Wei Jiang, Feng Liu, and Hongfu Sun. "DIP-UP: Deep Image Prior for Unwrapping Phase." Information 16, no. 7 (2025): 592. https://doi.org/10.3390/info16070592.

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Phase images from gradient echo MRI sequences reflect underlying magnetic field inhomogeneities but are inherently wrapped within the range of −π to π, requiring phase unwrapping to recover the true phase. In this study, we present DIP-UP (Deep Image Prior for Unwrapping Phase), a framework designed to refine two pre-trained deep learning models for phase unwrapping: PHUnet3D and PhaseNet3D. We compared the DIP-refined models to their original versions, as well as to the conventional PRELUDE algorithm from FSL, using both simulated and in vivo brain data. Results demonstrate that DIP refinemen

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Yao, Chen, and Yan Xia. "Deep Colorization for Surveillance Images." MATEC Web of Conferences 228 (2018): 02009. http://dx.doi.org/10.1051/matecconf/201822802009.

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In video surveillance application, grayscale image often influences the image processing results. In order to solve the colorization problem for surveillance images, this paper propose a fully end-to-end approach to obtain a reasonable colorization results. A CNN learning structure and gradient prior are be used for chromatic space inferring. Finally, our experimental results show our advantage.

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31

Zhao, Di, Li-Zhi Zhao, Yong-Jin Gan, and Bin-Yi Qin. "Undersampled magnetic resonance image reconstruction based on support prior and deep image prior without pre-training." Acta Physica Sinica 71, no. 5 (2022): 058701. http://dx.doi.org/10.7498/aps.71.20211761.

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Magnetic resonance imaging (MRI) method based on deep learning needs large-quantity and high-quality patient-based datasets for pre-training. However, this is a challenge to the clinical applications because it is difficult to obtain a sufficient quantity of patient-based MR datasets due to the limitation of equipment and patient privacy concerns. In this paper, we propose a novel undersampled MRI reconstruction method based on deep learning. This method does not require any pre-training procedures and does not depend on training datasets. The proposed method is inspired by the traditional dee

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32

Zhao, Di, Yanhu Huang, Feng Zhao, Binyi Qin, and Jincun Zheng. "Reference-Driven Undersampled MR Image Reconstruction Using Wavelet Sparsity-Constrained Deep Image Prior." Computational and Mathematical Methods in Medicine 2021 (January 20, 2021): 1–12. http://dx.doi.org/10.1155/2021/8865582.

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Deep learning has shown potential in significantly improving performance for undersampled magnetic resonance (MR) image reconstruction. However, one challenge for the application of deep learning to clinical scenarios is the requirement of large, high-quality patient-based datasets for network training. In this paper, we propose a novel deep learning-based method for undersampled MR image reconstruction that does not require pre-training procedure and pre-training datasets. The proposed reference-driven method using wavelet sparsity-constrained deep image prior (RWS-DIP) is based on the DIP fr

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Wang, Haijun, Wenli Zheng, Yaowei Wang, Tengfei Yang, Kaibing Zhang, and Youlin Shang. "Single hyperspectral image super-resolution using a progressive upsampling deep prior network." Electronic Research Archive 32, no. 7 (2024): 4517–42. http://dx.doi.org/10.3934/era.2024205.

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<p>Hyperspectral image super-resolution (SR) aims to enhance the spectral and spatial resolution of remote sensing images, enabling more accurate and detailed analysis of ground objects. However, hyperspectral images have high dimensional characteristics and complex spectral patterns. As a result, it is critical to effectively leverage the spatial non-local self-similarity and spectral correlation within hyperspectral images. To address this, we have proposed a novel single hyperspectral image SR method based on a progressive upsampling deep prior network. Specifically, we introduced the

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Yang Aiping, 杨爱萍, 王金斌 Wang Jinbin, 杨炳旺 Yang Bingwang, and 何宇清 He Yuqing. "Joint Deep Denoising Prior for Image Blind Deblurring." Acta Optica Sinica 38, no. 10 (2018): 1010003. http://dx.doi.org/10.3788/aos201838.1010003.

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Saghi, Zineb, Laure Guetaz, Thomas David, Philippe Ciuciu, and Zineb Saghi. "Deep image prior for limited-angle electron tomography." BIO Web of Conferences 129 (2024): 02012. http://dx.doi.org/10.1051/bioconf/202412902012.

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Albisani, Chiara, Daniele Baracchi, Alessandro Piva, and Fabrizio Argenti. "Self-Supervised SAR Despeckling Using Deep Image Prior." Pattern Recognition Letters 190 (April 2025): 169–76. https://doi.org/10.1016/j.patrec.2025.02.021.

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Hartanto, Cahyo Adhi, and Laksmita Rahadianti. "Single Image Dehazing Using Deep Learning." JOIV : International Journal on Informatics Visualization 5, no. 1 (2021): 76. http://dx.doi.org/10.30630/joiv.5.1.431.

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Many real-world situations such as bad weather may result in hazy environments. Images captured in these hazy conditions will have low image quality due to microparticles in the air. The microparticles light to scatter and absorb, resulting in hazy images with various effects. In recent years, image dehazing has been researched in depth to handle images captured in these conditions. Various methods were developed, from traditional methods to deep learning methods. Traditional methods focus more on the use of statistical prior. These statistical prior have weaknesses in certain conditions. This

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Sun, Yanglin, Jianjun Liu, Jinlong Yang, Zhiyong Xiao, and Zebin Wu. "A deep image prior-based interpretable network for hyperspectral image fusion." Remote Sensing Letters 12, no. 12 (2021): 1250–59. http://dx.doi.org/10.1080/2150704x.2021.1979270.

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胡, 锦华. "Salt-and-Pepper Noise Image Denoising Based on Deep Image Prior." Advances in Applied Mathematics 13, no. 06 (2024): 2734–41. http://dx.doi.org/10.12677/aam.2024.136262.

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Qiu, Yuanhong, Shuanlong Niu, Tongzhi Niu, Weifeng Li, and Bin Li. "Joint-Prior-Based Uneven Illumination Image Enhancement for Surface Defect Detection." Symmetry 14, no. 7 (2022): 1473. http://dx.doi.org/10.3390/sym14071473.

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Images in real surface defect detection scenes often suffer from uneven illumination. Retinex-based image enhancement methods can effectively eliminate the interference caused by uneven illumination and improve the visual quality of such images. However, these methods suffer from the loss of defect-discriminative information and a high computational burden. To address the above issues, we propose a joint-prior-based uneven illumination enhancement (JPUIE) method. Specifically, a semi-coupled retinex model is first constructed to accurately and effectively eliminate uneven illumination. Further

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Zhou, Hao, Huajun Feng, Wenbin Xu, Zhihai Xu, Qi Li, and Yueting Chen. "Deep denoiser prior based deep analytic network for lensless image restoration." Optics Express 29, no. 17 (2021): 27237. http://dx.doi.org/10.1364/oe.432544.

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Kim, Sunwoo, Soohyun Kim, and Seungryong Kim. "Deep Translation Prior: Test-Time Training for Photorealistic Style Transfer." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (2022): 1183–91. http://dx.doi.org/10.1609/aaai.v36i1.20004.

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Recent techniques to solve photorealistic style transfer within deep convolutional neural networks (CNNs) generally require intensive training from large-scale datasets, thus having limited applicability and poor generalization ability to unseen images or styles. To overcome this, we propose a novel framework, dubbed Deep Translation Prior (DTP), to accomplish photorealistic style transfer through test-time training on given input image pair with untrained networks, which learns an image pair-specific translation prior and thus yields better performance and generalization. Tailored for such te

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Luo, Zhijian, Siyu Chen, and Yuntao Qian. "Learning deep optimizer for blind image deconvolution." International Journal of Wavelets, Multiresolution and Information Processing 17, no. 06 (2019): 1950044. http://dx.doi.org/10.1142/s0219691319500449.

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In blind image deconvolution, priors are often leveraged to constrain the solution space, so as to alleviate the under-determinacy. Priors which are trained separately from the task of deconvolution tend to be unstable. We propose the Golf Optimizer, a novel but simple form of network that learns deep priors from data with better propagation behavior. Like playing golf, our method first estimates an aggressive propagation towards optimum using one network, and recurrently applies a residual CNN to learn the gradient of prior for delicate correction on restoration. Experiments show that our net

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Jung, SeHee, SungMin Yang, Eunseok Lee, et al. "Estimation of Particulate Levels Using Deep Dehazing Network and Temporal Prior." Journal of Sensors 2020 (July 7, 2020): 1–9. http://dx.doi.org/10.1155/2020/8841811.

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Particulate matters (PM) have become one of the important pollutants that deteriorate public health. Since PM is ubiquitous in the atmosphere, it is closely related to life quality in many different ways. Thus, a system to accurately monitor PM in diverse environments is imperative. Previous studies using digital images have relied on individual atmospheric images, not benefiting from both spatial and temporal effects of image sequences. This weakness led to undermining predictive power. To address this drawback, we propose a predictive model using the deep dehazing cascaded CNN and temporal p

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He, Yifan, Wei Cao, Xiaofeng Du, and Changlin Chen. "Internal Learning for Image Super-Resolution by Adaptive Feature Transform." Symmetry 12, no. 10 (2020): 1686. http://dx.doi.org/10.3390/sym12101686.

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Recent years have witnessed the great success of image super-resolution based on deep learning. However, it is hard to adapt a well-trained deep model for a specific image for further improvement. Since the internal repetition of patterns is widely observed in visual entities, internal self-similarity is expected to help improve image super-resolution. In this paper, we focus on exploiting a complementary relation between external and internal example-based super-resolution methods. Specifically, we first develop a basic network learning external prior from large scale training data and then l

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Petrovskaia, Anna, Raghavendra Jana, and Ivan Oseledets. "A Single Image Deep Learning Approach to Restoration of Corrupted Landsat-7 Satellite Images." Sensors 22, no. 23 (2022): 9273. http://dx.doi.org/10.3390/s22239273.

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Remote sensing is increasingly recognized as a convenient tool with a wide variety of uses in agriculture. Landsat-7 has supplied multi-spectral imagery of the Earth’s surface for more than 4 years and has become an important data source for a large number of research and policy-making initiatives. Unfortunately, a scan line corrector (SLC) on Landsat-7 broke down in May 2003, which caused the loss of up to 22 percent of any given scene. We present a single-image approach based on leveraging the abilities of the deep image prior method to fill in gaps using only the corrupt image. We test the

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Hari, Unnikrishnan, Alla Bukshu Bajulunisha, Pramod Pandey, et al. "Enhancing single image dehazing with self-supervised convolutional neural network and dark channel prior integration." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 1 (2025): 520. http://dx.doi.org/10.11591/ijece.v15i1.pp520-528.

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The removal of noise from images holds great significance as clear and denoised images are vital for various applications. Recent research efforts have been concentrated on the dehazing of single images. While conventional methods and deep learning approaches have been employed for daytime images, learning-based techniques have shown impressive dehazing results, albeit often with increased complexity. This has led to the persistence of prior-based methods, despite their slightly lower performance. To address this issue, we propose a novel deep learning-based dehazing method utilizing a self-su

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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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Lin, Jian, Qiurong Yan, Shang Lu, Yongjian Zheng, Shida Sun, and Zhen Wei. "A Compressed Reconstruction Network Combining Deep Image Prior and Autoencoding Priors for Single-Pixel Imaging." Photonics 9, no. 5 (2022): 343. http://dx.doi.org/10.3390/photonics9050343.

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Single-pixel imaging (SPI) is a promising imaging scheme based on compressive sensing. However, its application in high-resolution and real-time scenarios is a great challenge due to the long sampling and reconstruction required. The Deep Learning Compressed Network (DLCNet) can avoid the long-time iterative operation required by traditional reconstruction algorithms, and can achieve fast and high-quality reconstruction; hence, Deep-Learning-based SPI has attracted much attention. DLCNets learn prior distributions of real pictures from massive datasets, while the Deep Image Prior (DIP) uses a

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Xu, Shaoping, Xiaojun Chen, Yiling Tang, Shunliang Jiang, Xiaohui Cheng, and Nan Xiao. "Learning from Multiple Instances: A Two-Stage Unsupervised Image Denoising Framework Based on Deep Image Prior." Applied Sciences 12, no. 21 (2022): 10767. http://dx.doi.org/10.3390/app122110767.

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Supervised image denoising methods based on deep neural networks require a large amount of noisy-clean or noisy image pairs for network training. Thus, their performance drops drastically when the given noisy image is significantly different from the training data. Recently, several unsupervised learning models have been proposed to reduce the dependence on training data. Although unsupervised methods only require noisy images for learning, their denoising effect is relatively weak compared with supervised methods. This paper proposes a two-stage unsupervised deep learning framework based on d

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