arXiv Computer Vision
Matching Particle-Arrival Rates for Low-Count Image Denoising
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arXiv Computer Vision
Sep 3
Physics-Driven Independent Pair Generation for Iterative Self-Supervised Low-Dose CT Denoising
The paper introduces a physics‑driven, cross‑domain iterative framework for self‑supervised low‑dose CT denoising. It first uses a learned sinogram prior and the LDCT noise model to separate Poisson and Gaussian noise components, then applies binomial and Gaussian data thinning to create two training pairs with independent noise realizations. These pairs train an image‑domain network whose outputs are forward‑projected to refine the prior, yielding consistent performance gains over existing self‑supervised baselines and comparable results to supervised methods.
arXiv Computer Vision
Aug 26
Progressive Masked Refinement Self-supervised Learning for Low-Dose CT Denoising
arXiv:2601.14180v5 Announce Type: replace Abstract: Self-supervised learning has been increasingly investigated for low-dose computed tomography (LDCT) image denoising, as it alleviates the dependenc...
arXiv Machine Learning
4d ago
Learning Clean Generative Processes from Noisy Measurements
The paper investigates learning a generative model for clean images from only noisy observations. It shows that Noise2Noise regression can learn posterior predictions at or above the training noise level, but to predict below that level a consistency constraint is required. The authors derive conditions under which this consistency uniquely determines the continuation to the clean image distribution and validate the approach on CIFAR-10, photon‑counting microscopy, and synthetic aperture radar data.
Hugging Face Trending Papers
Oct 13
Noise2Match: Self-Supervised Low-Dose CT Denoising via Adaptive Inter-Slice Matching
Noise2Match is a self‑supervised method for low‑dose CT denoising that uses an adaptive inter‑slice matching network to model and correct the label transition between adjacent slices. By ensuring the equal‑mean condition of the Noise2Noise framework, it theoretically matches supervised training with paired noisy‑clean data. The approach also extends effectively to low‑dose cone‑beam CT, outperforming existing image‑domain self‑supervised methods.
arXiv Computer Vision
Sep 30
Learning to Recorrupt: Noise Distribution Agnostic Self-Supervised Image Denoising
arXiv:2603.25869v2 Announce Type: replace-cross Abstract: Self-supervised image denoising methods have traditionally relied on architectural constraints, pseudo-pair constructions, or specialized los...
arXiv Machine Learning
Jun 30
A Zero-Shot Deep Image Prior Framework for Denoising and Deconvolution in Fluorescence Microscopy
arXiv:2606. 28431v1 Announce Type: cross Abstract: Fluorescence microscopy images are degraded by noise and diffraction-induced blur, which compromise structural fidelity and limit quantitative analysis.