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

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Showing 201–225 of 7282 papers

TitleStatusHype
Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model InferenceCode2
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent SpacesCode2
DiffusionTrack: Diffusion Model For Multi-Object TrackingCode2
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq ModelCode2
FaceScore: Benchmarking and Enhancing Face Quality in Human GenerationCode2
Fixed Point Diffusion ModelsCode2
Diffusion Transformer PolicyCode2
FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive LearningCode2
FRAG: Frequency Adapting Group for Diffusion Video EditingCode2
AnoDDPM: Anomaly Detection With Denoising Diffusion Probabilistic Models Using Simplex NoiseCode2
Anomaly Detection with Conditioned Denoising Diffusion ModelsCode2
FreeInit: Bridging Initialization Gap in Video Diffusion ModelsCode2
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image GenerationCode2
Beyond Text: Frozen Large Language Models in Visual Signal ComprehensionCode2
CGVQM+D: Computer Graphics Video Quality Metric and DatasetCode2
GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New InsightsCode2
Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory SharpeningCode2
Diffusion Prior-Based Amortized Variational Inference for Noisy Inverse ProblemsCode2
Invisible Image Watermarks Are Provably Removable Using Generative AICode2
Diffusion Predictive Control with ConstraintsCode2
Generative Time Series Forecasting with Diffusion, Denoise, and DisentanglementCode2
Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-DenoisingCode2
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
A Geometric Perspective on Diffusion ModelsCode2
Diffusion Models in Vision: A SurveyCode2
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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