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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3276–3300 of 7282 papers

TitleStatusHype
CDSA: Conservative Denoising Score-based Algorithm for Offline Reinforcement Learning—0
Tuning-Free Visual Customization via View Iterative Self-Attention ControlCode0
Cometh: A continuous-time discrete-state graph diffusion model—0
Observation Denoising in CYRUS Soccer Simulation 2D Team For RoboCup 2024Code0
Improving Antibody Design with Force-Guided Sampling in Diffusion Models—0
PaRa: Personalizing Text-to-Image Diffusion via Parameter Rank Reduction—0
An Investigation of Noise Robustness for Flow-Matching-Based Zero-Shot TTS—0
Metric Convolutions: A Unifying Theory to Adaptive Convolutions—0
GenzIQA: Generalized Image Quality Assessment using Prompt-Guided Latent Diffusion Models—0
SC2: Towards Enhancing Content Preservation and Style Consistency in Long Text Style TransferCode0
URGENT Challenge: Universality, Robustness, and Generalizability For Speech Enhancement—0
Zero-Shot Video Editing through Adaptive Sliding Score Distillation—0
DiffusionPID: Interpreting Diffusion via Partial Information Decomposition—0
Diffusion Models in De Novo Drug Design—0
ReDistill: Residual Encoded Distillation for Peak Memory Reduction—0
Informed Graph Learning By Domain Knowledge Injection and Smooth Graph Signal RepresentationCode0
RadBARTsum: Domain Specific Adaption of Denoising Sequence-to-Sequence Models for Abstractive Radiology Report Summarization—0
SelfReDepth: Self-Supervised Real-Time Depth Restoration for Consumer-Grade SensorsCode0
A Self-Supervised Denoising Strategy for Underwater Acoustic Camera Imageries—0
AMOSL: Adaptive Modality-wise Structure Learning in Multi-view Graph Neural Networks For Enhanced Unified Representation—0
Graph Adversarial Diffusion ConvolutionCode0
Generating Synthetic Net Load Data with Physics-informed Diffusion Model—0
S2-Track: A Simple yet Strong Approach for End-to-End 3D Multi-Object Tracking—0
M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection via Multimodal Denoising—0
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models—0
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All datasetsDarmstadt Noise DatasetDIV2KDNDiris

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