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 1151–1175 of 7282 papers

TitleStatusHype
Devil is in the Uniformity: Exploring Diverse Learners within Transformer for Image RestorationCode1
CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot ClassificationCode1
CLIP-Diffusion-LM: Apply Diffusion Model on Image CaptioningCode1
DFormer: Diffusion-guided Transformer for Universal Image SegmentationCode1
clip2latent: Text driven sampling of a pre-trained StyleGAN using denoising diffusion and CLIPCode1
Collaborative Filtering Based on Diffusion Models: Unveiling the Potential of High-Order ConnectivityCode1
A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game EncodingCode1
Designing and Training of A Dual CNN for Image DenoisingCode1
DenoSent: A Denoising Objective for Self-Supervised Sentence Representation LearningCode1
DermoSegDiff: A Boundary-aware Segmentation Diffusion Model for Skin Lesion DelineationCode1
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
CLEARER: Multi-Scale Neural Architecture Search for Image RestorationCode1
Computing Multiple Image Reconstructions with a Single HypernetworkCode1
Diffusion Model as Representation LearnerCode1
Diffusion Probabilistic Priors for Zero-Shot Low-Dose CT Image DenoisingCode1
CleanUMamba: A Compact Mamba Network for Speech Denoising using Channel PruningCode1
Anomaly Detection using Score-based Perturbation ResilienceCode1
Denoising Point Clouds in Latent Space via Graph Convolution and Invertible Neural NetworkCode1
Combating Bilateral Edge Noise for Robust Link PredictionCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Combinatorial Complex Score-based Diffusion Modelling through Stochastic Differential EquationsCode1
Diffusion Models for Constrained DomainsCode1
Diffusion Models for Counterfactual Generation and Anomaly Detection in Brain ImagesCode1
Denoising of 3D MR images using a voxel-wise hybrid residual MLP-CNN model to improve small lesion diagnostic confidenceCode1
Show:102550
← PrevPage 47 of 292Next →
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