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 76100 of 7282 papers

TitleStatusHype
VideoTetris: Towards Compositional Text-to-Video GenerationCode3
Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference OptimizationCode3
Proxy Denoising for Source-Free Domain AdaptationCode3
Reservoir History Matching of the Norne field with generative exotic priors and a coupled Mixture of Experts -- Physics Informed Neural Operator Forward ModelCode3
MotionFollower: Editing Video Motion via Lightweight Score-Guided DiffusionCode3
On the Trajectory Regularity of ODE-based Diffusion SamplingCode3
UnMarker: A Universal Attack on Defensive Image WatermarkingCode3
Taming Stable Diffusion for Text to 360° Panorama Image GenerationCode3
Self-Rectifying Diffusion Sampling with Perturbed-Attention GuidanceCode3
Make-Your-Anchor: A Diffusion-based 2D Avatar Generation FrameworkCode3
Physics-Informed Diffusion ModelsCode3
ReNoise: Real Image Inversion Through Iterative NoisingCode3
AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and ModulationCode3
Multimodal-Conditioned Latent Diffusion Models for Fashion Image EditingCode3
Scaling Diffusion Models to Real-World 3D LiDAR Scene CompletionCode3
VmambaIR: Visual State Space Model for Image RestorationCode3
Score-Guided Diffusion for 3D Human RecoveryCode3
Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force FieldsCode3
Diffusion-TS: Interpretable Diffusion for General Time Series GenerationCode3
ViewDiff: 3D-Consistent Image Generation with Text-to-Image ModelsCode3
Seamless Human Motion Composition with Blended Positional EncodingsCode3
Visual Style Prompting with Swapping Self-AttentionCode3
3D Diffuser Actor: Policy Diffusion with 3D Scene RepresentationsCode3
3D Diffuser Actor: Policy Diffusion with 3D Scene RepresentationsCode3
RoHM: Robust Human Motion Reconstruction via DiffusionCode3
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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