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Found 8 papers, 3 papers with code
Transfer CLIP for Generalizable Image Denoising
Image denoising is a fundamental task in computer vision.
Image Denoising via Style Disentanglement
This leads to the separation of clean contents from noise, effectively denoising the image.
Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains
In this paper, we propose an unsupervised MAR method based on the diffusion model, a generative model with a high capacity to represent data distributions.
Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising
By considering the deep variational image posterior with a Gaussian form, score priors are extracted based on easily accessible minimum MSE Non-$i. i. d$ Gaussian denoisers and variational samples, which in turn facilitate optimizing the variational image posterior.
Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image Denoising
The results demonstrate that our method outperforms the state-of-the-art unsupervised method and surpasses several supervised deep learning-based methods.
Spectral Bayesian Uncertainty for Image Super-Resolution
In this paper, we propose to quantify spectral Bayesian uncertainty in image SR. To achieve this, a Dual-Domain Learning (DDL) framework is first proposed.
Self-Supervised Image Prior Learning With GMM From a Single Noisy Image
It can simultaneously achieve the noise level estimation and the image prior learning directly from only a single noisy image.
Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response
Quantification of local metabolic tumor volume (MTV) chan-ges after Chemo-radiotherapy would allow accurate tumor response evaluation.
