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 1301–1325 of 7282 papers

TitleStatusHype
Conditional GAN for Enhancing Diffusion Models in Efficient and Authentic Global Gesture Generation from Audios—0
Equivariant Blurring Diffusion for Hierarchical Molecular Conformer GenerationCode0
Decoding Diffusion: A Scalable Framework for Unsupervised Analysis of Latent Space Biases and Representations Using Natural Language Prompts—0
A Flow-based Truncated Denoising Diffusion Model for Super-resolution Magnetic Resonance Spectroscopic Imaging—0
Inferring Neural Signed Distance Functions by Overfitting on Single Noisy Point Clouds through Finetuning Data-Driven based PriorsCode0
High Resolution Seismic Waveform Generation using Denoising Diffusion—0
Generation of synthetic financial time series by diffusion models—0
Noise Adaption Network for Morse Code Image ClassificationCode0
Optimising image capture for low-light widefield quantitative fluorescence microscopy—0
Stable Consistency Tuning: Understanding and Improving Consistency ModelsCode0
Rectified Diffusion Guidance for Conditional Generation—0
3D-Adapter: Geometry-Consistent Multi-View Diffusion for High-Quality 3D GenerationCode3
Retrieval-Augmented Diffusion Models for Time Series ForecastingCode2
Denoising diffusion probabilistic models are optimally adaptive to unknown low dimensionality—0
WARP-LCA: Efficient Convolutional Sparse Coding with Locally Competitive Algorithm—0
FairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation—0
Semi-Implicit Functional Gradient Flow for Efficient Sampling—0
ELAICHI: Enhancing Low-resource TTS by Addressing Infrequent and Low-frequency Character Bigrams—0
Unsupervised Low-dose CT Reconstruction with One-way Conditional Normalizing Flows—0
AdaDiffSR: Adaptive Region-aware Dynamic Acceleration Diffusion Model for Real-World Image Super-Resolution—0
Deep Generative Models for 3D Medical Image Synthesis—0
Diffusion Priors for Variational Likelihood Estimation and Image DenoisingCode1
PGDiffSeg: Prior-Guided Denoising Diffusion Model with Parameter-Shared Attention for Breast Cancer Segmentation—0
Denoise-I2W: Mapping Images to Denoising Words for Accurate Zero-Shot Composed Image RetrievalCode0
DENOASR: Debiasing ASRs through Selective Denoising—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