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 1401–1425 of 7282 papers

TitleStatusHype
Efficient Diffusion Transformer with Step-wise Dynamic Attention MediatorsCode1
Noise Conditional Variational Score DistillationCode1
IterativePFN: True Iterative Point Cloud FilteringCode1
Joint Graph Rewiring and Feature Denoising via Spectral ResonanceCode1
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstructionCode1
Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot ClassificationCode1
Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image AttenuationCode1
Efficient Multi-Stage Video Denoising with Recurrent Spatio-Temporal FusionCode1
Efficient View Synthesis and 3D-based Multi-Frame Denoising with Multiplane Feature RepresentationsCode1
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse ProblemsCode1
Elucidated Rolling Diffusion Models for Probabilistic Weather ForecastingCode1
Decoupled Data Consistency with Diffusion Purification for Image RestorationCode1
Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance ScaleCode1
ISCL: Interdependent Self-Cooperative Learning for Unpaired Image DenoisingCode1
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image PriorCode1
E-MLB: Multilevel Benchmark for Event-Based Camera DenoisingCode1
End-to-End Learning for Joint Image Demosaicing, Denoising and Super-ResolutionCode1
End-to-End Diffusion Latent Optimization Improves Classifier GuidanceCode1
Decoder Denoising Pretraining for Semantic SegmentationCode1
TTS-Portuguese Corpus: a corpus for speech synthesis in Brazilian PortugueseCode1
Invertible Denoising Network: A Light Solution for Real Noise RemovalCode1
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy LabelsCode1
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable DiffusionCode1
Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting SurfacesCode1
Memory-Efficient Hierarchical Neural Architecture Search for Image DenoisingCode1
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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