Results 1 to 10 of about 1,132,388 (176)
A Denoising Method Using Deep Image Prior to Human-Target Detection Using MIMO FMCW Radar [PDF]
Sensors, 2022 A Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous Wave (FMCW) radar can provide a range-angle map that expresses the signal power against each range and angle. It is possible to estimate object locations by detecting the signal power
Koji Endo +2 more
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Low-Light Image Enhancement via Retinex-Style Decomposition of Denoised Deep Image Prior [PDF]
Sensors, 2022 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.
Xianjie Gao +2 more
doaj +2 more sources
MR image reconstruction using deep density priors [PDF]
IEEE Transactions on Medical Imaging, 2018 Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data.
Baumgartner, Christian F. +4 more
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Deep image prior for undersampling high-speed photoacoustic microscopy [PDF]
Photoacoustics, 2021 Photoacoustic microscopy (PAM) is an emerging imaging method combining light and sound. However, limited by the laser’s repetition rate, state-of-the-art high-speed PAM technology often sacrifices spatial sampling density (i.e., undersampling) for ...
Tri Vu +12 more
doaj +2 more sources
Deep Image Prior Amplitude SAR Image Anonymization
Remote Sensing, 2023 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 ...
Edoardo Daniele Cannas +4 more
doaj +2 more sources
Image Restoration using Total Variation Regularized Deep Image Prior [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018 In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction. Traditional regularizers, such as total variation (TV), rely on analytical models of sparsity.
Kamilov, Ulugbek S. +3 more
core +2 more sources
Diffraction tomography with a deep image prior. [PDF]
Opt Express, 2020 We present a tomographic imaging technique, termed Deep Prior Diffraction Tomography (DP-DT), to reconstruct the 3D refractive index (RI) of thick biological samples at high resolution from a sequence of low-resolution images collected under angularly varying illumination.
Zhou KC, Horstmeyer R.
europepmc +4 more sources
PET Image Reconstruction Using Deep Image Prior. [PDF]
IEEE Trans Med Imaging, 2019 Recently, deep neural networks have been widely and successfully applied in computer vision tasks and have attracted growing interest in medical imaging. One barrier for the application of deep neural networks to medical imaging is the need for large amounts of prior training pairs, which is not always feasible in clinical practice.
Gong K, Catana C, Qi J, Li Q.
europepmc +6 more sources
Noise-Resistant Demosaicing with Deep Image Prior Network and Random RGBW Color Filter Array [PDF]
Sensors, 2022 In this paper, we propose a deep-image-prior-based demosaicing method for a random RGBW color filter array (CFA). The color reconstruction from the random RGBW CFA is performed by the deep image prior network, which uses only the RGBW CFA image as the ...
Edwin Kurniawan, Yunjin Park, Sukho Lee
doaj +2 more sources
Joint Demosaicing and Denoising Based on a Variational Deep Image Prior Neural Network [PDF]
Sensors, 2020 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.
Yunjin Park +3 more
doaj +2 more sources