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 2826–2850 of 7282 papers

TitleStatusHype
Learning in Deep Factor Graphs with Gaussian Belief PropagationCode0
AdaDiff: Adaptive Step Selection for Fast Diffusion Models—0
ToddlerDiffusion: Interactive Structured Image Generation with Cascaded Schrödinger Bridge—0
Highly Detailed and Temporal Consistent Video Stylization via Synchronized Multi-Frame Diffusion—0
EEG Connectivity Analysis Using Denoising Autoencoders for the Detection of Dyslexia—0
D-SCo: Dual-Stream Conditional Diffusion for Monocular Hand-Held Object Reconstruction—0
ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion ModelsCode0
Continual Learning of Diffusion Models with Generative DistillationCode1
TDiffDe: A Truncated Diffusion Model for Remote Sensing Hyperspectral Image Denoising—0
Single Image Compressed Sensing MRI via a Self-Supervised Deep Denoising Approach—0
Recognition-Guided Diffusion Model for Scene Text Image Super-Resolution—0
MetaCloak: Preventing Unauthorized Subject-driven Text-to-image Diffusion-based Synthesis via Meta-learningCode1
DiffusionMat: Alpha Matting as Sequential Refinement Learning—0
Diffusion360: Seamless 360 Degree Panoramic Image Generation based on Diffusion ModelsCode2
White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is?Code3
Using Human Feedback to Fine-tune Diffusion Models without Any Reward ModelCode2
FrePolad: Frequency-Rectified Point Latent Diffusion for Point Cloud Generation—0
Mixing-Denoising Generalizable Occupancy Networks—0
Gaussian Interpolation Flows—0
Sparse4D v3: Advancing End-to-End 3D Detection and TrackingCode2
An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis—0
GaussianDiffusion: 3D Gaussian Splatting for Denoising Diffusion Probabilistic Models with Structured Noise—0
DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion ModelCode0
SDDPM: Speckle Denoising Diffusion Probabilistic Models—0
FedDiff: Diffusion Model Driven Federated Learning for Multi-Modal and Multi-Clients—0
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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