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
DiffDA: a Diffusion Model for Weather-scale Data AssimilationCode1
k-Space Deep Learning for Accelerated MRICode1
Label-Efficient Semantic Segmentation with Diffusion ModelsCode1
Label-Noise Robust Diffusion ModelsCode1
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
DialogLM: Pre-trained Model for Long Dialogue Understanding and SummarizationCode1
DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein TracesCode1
CVF-SID: Cyclic multi-Variate Function for Self-Supervised Image Denoising by Disentangling Noise from ImageCode1
Learnability Enhancement for Low-light Raw Denoising: Where Paired Real Data Meets Noise ModelingCode1
Learning a Diffusion Model Policy from Rewards via Q-Score MatchingCode1
CutMIB: Boosting Light Field Super-Resolution via Multi-View Image BlendingCode1
Learning Deformable Kernels for Image and Video DenoisingCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Learning Graph-Convolutional Representations for Point Cloud DenoisingCode1
Learning low-rank latent mesoscale structures in networksCode1
Learning multi-scale local conditional probability models of imagesCode1
Learning Robust Recommender from Noisy Implicit FeedbackCode1
Crystal Structure Prediction by Joint Equivariant DiffusionCode1
Learning Signed Distance Functions from Noisy 3D Point Clouds via Noise to Noise MappingCode1
DiffO: Single-step Diffusion for Image Compression at Ultra-Low BitratesCode1
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy LabelsCode1
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT DenoisingCode1
Designing and Training of A Dual CNN 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