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 926–950 of 7282 papers

TitleStatusHype
T2ICount: Enhancing Cross-modal Understanding for Zero-Shot CountingCode1
UHD-processer: Unified UHD Image Restoration with Progressive Frequency Learning and Degradation-aware PromptsCode1
IM-Portrait: Learning 3D-aware Video Diffusion for Photorealistic Talking Heads from Monocular VideosC—0
All-Optical Nonlinear Diffractive Deep Network for Ultrafast Image Denoising—0
Encapsulated Composition of Text-to-Image and Text-to-Video Models for High-Quality Video Synthesis—0
Rethinking Reconstruction and Denoising in the Dark: New Perspective, General Architecture and BeyondCode0
Random Conditioning for Diffusion Model Compression with Distillation—0
Exploring Structured Semantic Priors Underlying Diffusion Score for Test-time AdaptationCode1
Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusion—0
CoordFlow: Coordinate Flow for Pixel-wise Neural Video Representation—0
Cached Adaptive Token Merging: Dynamic Token Reduction and Redundant Computation Elimination in Diffusion ModelCode0
Token Pruning for Caching Better: 9 Times Acceleration on Stable Diffusion for FreeCode0
A Study on Context Length and Efficient Transformers for Biomedical Image Analysis—0
S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain—0
KAE: Kolmogorov-Arnold Auto-Encoder for Representation LearningCode1
LTX-Video: Realtime Video Latent DiffusionCode9
Quantum Diffusion Model for Quark and Gluon Jet GenerationCode0
Edicho: Consistent Image Editing in the WildCode2
DDIM sampling for Generative AIBIM, a faster intelligent structural design frameworkCode1
VMix: Improving Text-to-Image Diffusion Model with Cross-Attention Mixing ControlCode2
"Generative Models for Financial Time Series Data: Enhancing Signal-to-Noise Ratio and Addressing Data Scarcity in A-Share Market—0
Single-image reflection removal via self-supervised diffusion models—0
MAKIMA: Tuning-free Multi-Attribute Open-domain Video Editing via Mask-Guided Attention Modulation—0
MaIR: A Locality- and Continuity-Preserving Mamba for Image RestorationCode2
Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling—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