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 501–525 of 7282 papers

TitleStatusHype
Diffusion Counterfactuals for Image RegressorsCode0
FireEdit: Fine-grained Instruction-based Image Editing via Region-aware Vision Language Model—0
PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion Models—0
BADGR: Bundle Adjustment Diffusion Conditioned by GRadients for Wide-Baseline Floor Plan Reconstruction—0
Dita: Scaling Diffusion Transformer for Generalist Vision-Language-Action PolicyCode2
Inference-Time Scaling for Flow Models via Stochastic Generation and Rollover Budget Forcing—0
Towards Robust Time-of-Flight Depth Denoising with Confidence-Aware Diffusion Model—0
Universal Architectures for the Learning of Polyhedral Norms and Convex Regularizers—0
HunyuanPortrait: Implicit Condition Control for Enhanced Portrait Animation—0
Hiding Images in Diffusion Models by Editing Learned Score FunctionsCode0
Dig2DIG: Dig into Diffusion Information Gains for Image Fusion—0
Training-free Diffusion Acceleration with Bottleneck Sampling—0
Unified Uncertainty-Aware Diffusion for Multi-Agent Trajectory Modeling—0
AIM2PC: Aerial Image to 3D Building Point Cloud ReconstructionCode0
Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos—0
Discriminative protein sequence modelling with Latent Space Diffusion—0
Video-T1: Test-Time Scaling for Video Generation—0
Unified Geometry and Color Compression Framework for Point Clouds via Generative Diffusion Priors—0
SNRAware: Improved Deep Learning MRI Denoising with SNR Unit Training and G-factor Map Augmentation—0
Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration—0
Guidance Free Image Editing via Explicit Conditioning—0
Aligning Foundation Model Priors and Diffusion-Based Hand Interactions for Occlusion-Resistant Two-Hand Reconstruction—0
Efficient Diffusion Training through Parallelization with Truncated Karhunen-Loève Expansion—0
Towards Transformer-Based Aligned Generation with Self-Coherence Guidance—0
Accelerated Cardiac Parametric Mapping using Deep Learning-Refined Subspace ModelsCode0
Show:102550
← PrevPage 21 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