SOTAVerified

Denoising

Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.

( Image credit: Beyond a Gaussian Denoiser )

Papers

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Showing 1–10 of 7282 papers

TitleStatusHype
fastWDM3D: Fast and Accurate 3D Healthy Tissue InpaintingCode0
Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models—0
Similarity-Guided Diffusion for Contrastive Sequential Recommendation—0
HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing—0
AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air—0
A statistical physics framework for optimal learning—0
Leveraging the Spatial Hierarchy: Coarse-to-fine Trajectory Generation via Cascaded Hybrid Diffusion—0
ScoreAdv: Score-based Targeted Generation of Natural Adversarial Examples via Diffusion ModelsCode1
LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language ModelsCode1
SPADE: Spatial-Aware Denoising Network for Open-vocabulary Panoptic Scene Graph Generation with Long- and Local-range Context Reasoning—0
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified