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 1001–1025 of 7282 papers

TitleStatusHype
HQ-50K: A Large-scale, High-quality Dataset for Image RestorationCode1
Normalization-Equivariant Neural Networks with Application to Image DenoisingCode1
Interpreting and Improving Diffusion Models from an Optimization PerspectiveCode1
Conditional Diffusion Models for Weakly Supervised Medical Image SegmentationCode1
DFormer: Diffusion-guided Transformer for Universal Image SegmentationCode1
Str2Str: A Score-based Framework for Zero-shot Protein Conformation SamplingCode1
Microscopy image reconstruction with physics-informed denoising diffusion probabilistic modelCode1
PLANNER: Generating Diversified Paragraph via Latent Language Diffusion ModelCode1
Bilevel Fast Scene Adaptation for Low-Light Image EnhancementCode1
Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise MappingCode1
DiffPack: A Torsional Diffusion Model for Autoregressive Protein Side-Chain PackingCode1
ACLM: A Selective-Denoising based Generative Data Augmentation Approach for Low-Resource Complex NERCode1
Protein Design with Guided Discrete DiffusionCode1
Synthetic CT Generation from MRI using 3D Transformer-based Denoising Diffusion ModelCode1
DyGen: Learning from Noisy Labels via Dynamics-Enhanced Generative ModelingCode1
Nested Diffusion Processes for Anytime Image GenerationCode1
Diffusion Model for Dense MatchingCode1
Make-An-Audio 2: Temporal-Enhanced Text-to-Audio GenerationCode1
CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion ModelsCode1
On Diffusion Modeling for Anomaly DetectionCode1
Conditional score-based diffusion models for Bayesian inference in infinite dimensionsCode1
Observation Denoising in CYRUS Soccer Simulation 2D Team For RoboCup 2023Code1
Parallel Sampling of Diffusion ModelsCode1
NAP: Neural 3D Articulation PriorCode1
UDPM: Upsampling Diffusion Probabilistic ModelsCode1
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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