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 1926–1950 of 7282 papers

TitleStatusHype
PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding—0
Graph Bottlenecked Social RecommendationCode1
DualBind: A Dual-Loss Framework for Protein-Ligand Binding Affinity Prediction—0
MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results—0
Generative Lifting of Multiview to 3D from Unknown Pose: Wrapping NeRF inside Diffusion—0
GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly DetectionCode2
fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functionsCode1
Unleashing the Denoising Capability of Diffusion Prior for Solving Inverse ProblemsCode1
AsyncDiff: Parallelizing Diffusion Models by Asynchronous DenoisingCode4
CDSA: Conservative Denoising Score-based Algorithm for Offline Reinforcement Learning—0
Stable Neighbor Denoising for Source-free Domain Adaptive SegmentationCode1
ProcessPainter: Learn Painting Process from Sequence DataCode2
Cometh: A continuous-time discrete-state graph diffusion model—0
FRAG: Frequency Adapting Group for Diffusion Video EditingCode2
Tuning-Free Visual Customization via View Iterative Self-Attention ControlCode0
Latent Diffusion Model-Enabled Low-Latency Semantic Communication in the Presence of Semantic Ambiguities and Wireless Channel NoisesCode1
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented GenerationCode1
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
A DeNoising FPN With Transformer R-CNN for Tiny Object DetectionCode2
MotionClone: Training-Free Motion Cloning for Controllable Video GenerationCode4
Medical Vision Generalist: Unifying Medical Imaging Tasks in ContextCode2
Metric Convolutions: A Unifying Theory to Adaptive Convolutions—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