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 4101–4125 of 7282 papers

TitleStatusHype
Sketch-Guided Text-to-Image Diffusion Models—0
Generative Joint Source-Channel Coding for Semantic Image Transmission—0
HouseDiffusion: Vector Floorplan Generation via a Diffusion Model with Discrete and Continuous DenoisingCode2
On the impact of incorporating task-information in learning-based image denoising—0
Inversion-Based Style Transfer with Diffusion ModelsCode2
TetraDiffusion: Tetrahedral Diffusion Models for 3D Shape GenerationCode1
Latent Video Diffusion Models for High-Fidelity Long Video GenerationCode2
A Neural-Network-Based Convex Regularizer for Inverse ProblemsCode1
Can denoising diffusion probabilistic models generate realistic astrophysical fields?Code0
Convolutional Neural Generative Coding: Scaling Predictive Coding to Natural Images—0
Person Image Synthesis via Denoising Diffusion ModelCode2
Adaptive Dynamic Filtering Network for Image DenoisingCode1
SinDiffusion: Learning a Diffusion Model from a Single Natural ImageCode2
DiffDreamer: Towards Consistent Unsupervised Single-view Scene Extrapolation with Conditional Diffusion Models—0
EDICT: Exact Diffusion Inversion via Coupled TransformationsCode2
D²Net: A Denoising and Dereverberation Network Based on Two-branch Encoder and Dual-path Transformer—0
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
3D Detection and Characterisation of ALMA Sources through Deep LearningCode1
Diffusion Denoising Process for Perceptron Bias in Out-of-distribution DetectionCode0
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse ProblemsCode1
IC3D: Image-Conditioned 3D Diffusion for Shape Generation—0
DiffStyler: Controllable Dual Diffusion for Text-Driven Image StylizationCode1
Denoising Multi-Similarity Formulation: A Self-paced Curriculum-Driven Approach for Robust Metric Learning—0
Exploring validation metrics for offline model-based optimisation with diffusion modelsCode0
GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property PredictionCode1
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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