Deep Image Prior
PulseAugur coverage of Deep Image Prior — every cluster mentioning Deep Image Prior across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
-
New theory and algorithms advance coherent imaging techniques
Researchers have developed a new theoretical framework and algorithms for multilook coherent imaging, a technique used in applications like digital holography and synthetic aperture radar. The paper provides the first t…
-
Unsupervised Deep Learning Solves Computed Tomography Inverse Problems
Researchers have developed a novel unsupervised deep learning framework for solving inverse problems in computed tomography, particularly when ground-truth data is unavailable. This method, termed "Deep Image Prior" (DI…
-
SE-UNet framework offers data-efficient image synthesis for inverse problems
Researchers have introduced SE-UNet, a novel framework for image synthesis that addresses the limitations of diffusion models in real-world inverse problems. This new method leverages geometric equivariance and singular…
-
New convolution-free architecture enhances image restoration tasks
Researchers have developed Pool-DIP, a new convolution-free architecture for image restoration tasks. This model efficiently captures spatial context using pooling-based contrast modeling, leading to improved denoising …
-
New MG-SpaIR framework offers training-data-free image restoration
Researchers have developed MG-SpaIR, a novel framework for image restoration that does not require training data. This method utilizes implicit neural representations (INRs) and a multi-grade residual hierarchy to progr…
-
Deep learning enhances 3D electron tomography reconstructions
Researchers have developed a new unsupervised deep learning approach called Deep Image Prior (DIP) to improve 3D reconstructions in electron tomography, particularly under challenging sparse-view and limited-angle condi…
-
New early stopping method improves Deep Image Prior for image restoration
Researchers have developed a new early stopping method for Deep Image Prior (DIP) to prevent overfitting in image restoration tasks. The approach constructs pseudo self-referenced images to mimic having two independent …
-
DIPLI framework enhances astronomical image restoration using deep learning
Researchers have developed DIPLI, a novel framework for restoring astronomical images that leverages Deep Image Prior (DIP) with multi-frame processing. Unlike traditional deep learning methods, DIPLI does not require l…