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 2801–2825 of 7282 papers

TitleStatusHype
A Flow-based Truncated Denoising Diffusion Model for Super-resolution Magnetic Resonance Spectroscopic Imaging—0
Stable Consistency Tuning: Understanding and Improving Consistency ModelsCode0
Denoising diffusion probabilistic models are optimally adaptive to unknown low dimensionality—0
Generation of synthetic financial time series by diffusion models—0
Noise Adaption Network for Morse Code Image ClassificationCode0
FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation—0
Rectified Diffusion Guidance for Conditional Generation—0
WARP-LCA: Efficient Convolutional Sparse Coding with Locally Competitive Algorithm—0
Optimising image capture for low-light widefield quantitative fluorescence microscopy—0
PGDiffSeg: Prior-Guided Denoising Diffusion Model with Parameter-Shared Attention for Breast Cancer Segmentation—0
Unsupervised Low-dose CT Reconstruction with One-way Conditional Normalizing Flows—0
Semi-Implicit Functional Gradient Flow for Efficient Sampling—0
Deep Generative Models for 3D Medical Image Synthesis—0
AdaDiffSR: Adaptive Region-aware Dynamic Acceleration Diffusion Model for Real-World Image Super-Resolution—0
ELAICHI: Enhancing Low-resource TTS by Addressing Infrequent and Low-frequency Character Bigrams—0
Test-time Adversarial Defense with Opposite Adversarial Path and High Attack Time Cost—0
Denoise-I2W: Mapping Images to Denoising Words for Accurate Zero-Shot Composed Image RetrievalCode0
DENOASR: Debiasing ASRs through Selective Denoising—0
MBD: Multi b-value Denoising of Diffusion Magnetic Resonance Images—0
DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization—0
SeaDAG: Semi-autoregressive Diffusion for Conditional Directed Acyclic Graph Generation—0
Traffic Matrix Estimation based on Denoising Diffusion Probabilistic ModelCode0
Multi-head Sequence Tagging Model for Grammatical Error CorrectionCode0
IKDP: Inverse Kinematics through Diffusion Process—0
MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applications—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