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

Showing 16511675 of 7282 papers

TitleStatusHype
CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion ModelCode1
Multi-Stage Raw Video Denoising with Adversarial Loss and Gradient MaskCode1
Masked Autoencoders as Image ProcessorsCode1
A zero-inflated gamma model for deconvolved calcium imaging tracesCode1
Unified Discrete Diffusion for Categorical DataCode1
Deep Multi-Threshold Spiking-UNet for Image ProcessingCode1
Importance of Aligning Training Strategy with Evaluation for Diffusion Models in 3D Multiclass SegmentationCode1
Stacked DeBERT: All Attention in Incomplete Data for Text ClassificationCode1
Deep Speech Synthesis from MRI-Based Articulatory RepresentationsCode1
Deep Universal Blind Image DenoisingCode1
Alleviating Exposure Bias in Diffusion Models through Sampling with Shifted Time StepsCode1
Deformed2Self: Self-Supervised Denoising for Dynamic Medical ImagingCode1
Markup-to-Image Diffusion Models with Scheduled SamplingCode1
Masked Diffusion as Self-supervised Representation LearnerCode1
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion AttacksCode1
Improving Distantly-Supervised Named Entity Recognition with Self-Collaborative Denoising LearningCode1
Accelerated MRI with Un-trained Neural NetworksCode1
A deep network for sinogram and CT image reconstructionCode1
Deep Semantic Statistics Matching (D2SM) Denoising NetworkCode1
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word AlignmentCode1
MANet: Improving Video Denoising with a Multi-Alignment NetworkCode1
Accurate Image Restoration with Attention Retractable TransformerCode1
Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super ResolutionCode1
Stochastic Segmentation with Conditional Categorical Diffusion ModelsCode1
Can LLMs be Good Graph Judge for Knowledge Graph Construction?Code1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified