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

Showing 1–25 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
AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air—0
HUG-VAS: A Hierarchical NURBS-Based Generative Model for Aortic Geometry Synthesis and Controllable Editing—0
A statistical physics framework for optimal learning—0
Leveraging the Spatial Hierarchy: Coarse-to-fine Trajectory Generation via Cascaded Hybrid Diffusion—0
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
ScoreAdv: Score-based Targeted Generation of Natural Adversarial Examples via Diffusion ModelsCode1
Unconditional Diffusion for Generative Sequential RecommendationCode0
Hierarchical Intent-guided Optimization with Pluggable LLM-Driven Semantics for Session-based RecommendationCode0
CoT-lized Diffusion: Let's Reinforce T2I Generation Step-by-step—0
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather—0
FreeMorph: Tuning-Free Generalized Image Morphing with Diffusion Model—0
Energy-Based Transformers are Scalable Learners and ThinkersVerified5
EAMamba: Efficient All-Around Vision State Space Model for Image RestorationCode2
Score-Based Model for Low-Rank Tensor Recovery—0
From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging—0
Learning to See in the Extremely DarkCode2
Lightweight Physics-Informed Zero-Shot Ultrasound Plane Wave Denoising—0
SmoothSinger: A Conditional Diffusion Model for Singing Voice Synthesis with Multi-Resolution Architecture—0
Integrating Vehicle Acoustic Data for Enhanced Urban Traffic Management: A Study on Speed Classification in Suzhou—0
Ctrl-Z Sampling: Diffusion Sampling with Controlled Random Zigzag Explorations—0
TDiR: Transformer based Diffusion for Image Restoration Tasks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81—Unverified
2Pixel-shuffling DownsamplingPSNR38.4—Unverified
3TWSCPSNR37.93—Unverified
4CBDNet(Syn)PSNR37.57—Unverified
5MCWNNMPSNR37.38—Unverified
6Han et alPSNR35.95—Unverified
7FFDNetPSNR34.4—Unverified
8TNRDPSNR33.65—Unverified
9CDnCNN-BPSNR32.43—Unverified
10NLRNPSNR30.8—Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64—Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61—Unverified