Results 41 to 50 of about 225,507 (258)
Plug-and-Play Image Restoration With Deep Denoiser Prior [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 An extended version of IRCNN (CVPR17).
Zhang, Kai +5 more
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A Single Image Deep Learning Approach to Restoration of Corrupted Landsat-7 Satellite Images
Sensors, 2022 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 ...
Anna Petrovskaia +2 more
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The Spectral Bias of the Deep Image Prior
CoRR, 2019 The "deep image prior" proposed by Ulyanov et al. is an intriguing property of neural nets: a convolutional encoder-decoder network can be used as a prior for natural images. The network architecture implicitly introduces a bias; If we train the model to map white noise to a corrupted image, this bias guides the model to fit the true image before ...
Prithvijit Chakrabarty, Subhransu Maji
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Solar Speckle Image Deblurring With Deep Prior Constraint Based on Regularization
IEEE Access, 2022 The solar speckle image has the characteristics with single features, more noise, and blurred local details. Most of the existing deep learning deblurring methods for solar speckle images have some problems, such as high-frequency loss, artifact ...
Yahui Jin +5 more
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Dynamic PET Image Denoising Using Deep Image Prior Combined With Regularization by Denoising
IEEE Access, 2021 The quantitative accuracy of positron emission tomography (PET) is affected by several factors, including the intrinsic resolution of the imaging system and inherently noisy data, which result in a low signal-to-noise ratio (SNR) of PET image. To address
Hao Sun +5 more
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Anchored neighborhood deep network for single-image super-resolution
EURASIP Journal on Image and Video Processing, 2018 Real-time image and video processing is a challenging problem in smart surveillance applications. It is necessary to trade off between high frame rate and high resolution to meet the limited bandwidth requirement in many specific applications.
Wuzhen Shi +4 more
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Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior
Remote Sensing, 2021 The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio (SNR) of the measurements.
Lina Zhuang, Michael K. Ng, Xiyou Fu
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Towards the Automation of Deep Image Prior
CoRR, 2019 Single image inverse problem is a notoriously challenging ill-posed problem that aims to restore the original image from one of its corrupted versions. Recently, this field has been immensely influenced by the emergence of deep-learning techniques. Deep Image Prior (DIP) offers a new approach that forces the recovered image to be synthesized from a ...
Qianwei Zhou +5 more
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A Synergistic Approach to Image Restoration DenseNet Enhanced Deep Image Prior [PDF]
E3S Web of ConferencesThe 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.
A Senthil Anandhi., Jaiganesh M.
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Hyperspectral Mixed Noise Removal via Spatial-Spectral Constrained Unsupervised Deep Image Prior
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Recently, deep learning-based methods are proposed for hyperspectral images (HSIs) denoising. Among them, unsupervised methods such as deep image prior (DIP)-based methods have received much attention because these methods do not require any training ...
Yi-Si Luo +4 more
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