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 5026–5050 of 7282 papers

TitleStatusHype
SAR Image Synthesis with Diffusion Models—0
Satellite to GroundScape - Large-scale Consistent Ground View Generation from Satellite Views—0
SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation—0
Scalable AI Framework for Defect Detection in Metal Additive Manufacturing—0
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models—0
Scalable Lossless Coding of Dynamic Medical CT Data Using Motion Compensated Wavelet Lifting with Denoised Prediction and Update—0
Scalable Non-Cartesian Magnetic Resonance Imaging with R2D2—0
Scale-Agnostic Super-Resolution in MRI using Feature-Based Coordinate Networks—0
Zero-Shot Voice Conditioning for Denoising Diffusion TTS Models—0
Scale-wise Distillation of Diffusion Models—0
Accelerating Diffusion Models via Pre-segmentation Diffusion Sampling for Medical Image Segmentation—0
Accelerating Diffusion Language Model Inference via Efficient KV Caching and Guided Diffusion—0
Scaling Inference Time Compute for Diffusion Models—0
ScalingNoise: Scaling Inference-Time Search for Generating Infinite Videos—0
Scattering and Gathering for Spatially Varying Blurs—0
SCDM: Score-Based Channel Denoising Model for Digital Semantic Communications—0
Scene-Adapted Plug-and-Play Algorithm with Guaranteed Convergence: Applications to Data Fusion in Imaging—0
Scene-adapted plug-and-play algorithm with convergence guarantees—0
SceneDiffuser: Efficient and Controllable Driving Simulation Initialization and Rollout—0
SceneMI: Motion In-betweening for Modeling Human-Scene Interactions—0
Schedule On the Fly: Diffusion Time Prediction for Faster and Better Image Generation—0
Schrödinger Bridge Flow for Unpaired Data Translation—0
Schrödinger Bridge for Generative Speech Enhancement—0
Unsupervised Real-World Denoising: Sparsity is All You Need—0
Unsupervised Region-Based Image Editing of Denoising Diffusion Models—0
Show:102550
← PrevPage 202 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