PolarVSR is the first unified architecture for continuous space-time polarization video reconstruction from DoFP captures, using polarization-aware implicit neural representations, a flow-guided variation loss, and a new large-scale benchmark.
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Zero-shot image restora- tion using denoising diffusion null-space model.arXiv preprint arXiv:2212.00490
15 Pith papers cite this work. Polarity classification is still indexing.
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InvDiff-CGM uses invertible architectures in diffusion and U-Net plus a multi-scale prior injector to construct CGMs with 85% lower peak training memory and 38.02 dB PSNR on RadioMap3DSeer.
Diff-ANO uses conditional consistency models and adjoint neural operator surrogates to enable fast, high-quality USCT reconstructions under sparse and partial views by replacing slow PDE solvers and enabling few-step sampling.
COCO-Inpaint supplies a large-scale dataset and evaluation protocol focused on inpainting-based image forgeries to benchmark existing detection methods.
Conservative flows generate by running probability-preserving stochastic dynamics initialized at data points rather than noise, using corrected Langevin or predictor-corrector mechanisms on top of any pretrained flow model and showing gains on Swiss-roll, ImageNet-256 and Oxford Flowers-102.
PLMD applies a denoising diffusion model to predict labels for unknown map regions, allowing goal localization in unexplored environments by substituting completed labels into existing navigation pipelines.
Error in approximating the tangent conditional score by the unconditional score in diffusion models is bounded by dimension-free conditional mutual information, with a projected-Langevin method outperforming baselines in inpainting and super-resolution.
A degradation-aware diffusion framework fuses multimodal images under arbitrary degradations by directly regressing the fused image and applying joint degradation-fusion constraints during limited-step sampling.
Under a Gaussian prior assumption, zero-shot diffusion posterior samplers for inverse problems admit closed-form spectral representations that enable a new parameter-selection framework balancing perceptual quality and signal fidelity.
PAR is a multi-scale autoregressive transformer framework for protein backbone generation that uses coarse-to-fine prediction, noisy context learning, and flow-based decoding to achieve high-quality unconditional and zero-shot conditional outputs.
NPN introduces a neural-network-based regularization that promotes reconstructions lying in a low-dimensional projection of the sensing operator's null-space, with claimed theoretical guarantees and improved empirical performance across compressive sensing, deblurring, super-resolution, CT, and MRI.
Stochastic image enhancement methods are shown to be variants of a shared SDE differing in drift, diffusion, terminal distributions and boundary conditions, with controlled experiments revealing no single dominant family and a new modular library released.
A dual ascent optimization framework is introduced for MAP estimation with diffusion priors, claimed to outperform prior methods on image restoration in quality, noise robustness, speed, and data fidelity.
A survey that introduces taxonomies for categorizing pre-trained diffusion model methods applied to inverse problems and analyzes their connections and challenges.
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Plug-and-Play Label Map Diffusion for Universal Goal-Oriented Navigation
PLMD applies a denoising diffusion model to predict labels for unknown map regions, allowing goal localization in unexplored environments by substituting completed labels into existing navigation pipelines.