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 3426–3450 of 7282 papers

TitleStatusHype
BOOT: Data-free Distillation of Denoising Diffusion Models with Bootstrapping—0
Multi-Architecture Multi-Expert Diffusion Models—0
HQ-50K: A Large-scale, High-quality Dataset for Image RestorationCode1
SyncDiffusion: Coherent Montage via Synchronized Joint Diffusions—0
Convolutional Recurrent Neural Network with Attention for 3D Speech Enhancement—0
Interpreting and Improving Diffusion Models from an Optimization PerspectiveCode1
Normalization-Equivariant Neural Networks with Application to Image DenoisingCode1
Complexity-aware Large Scale Origin-Destination Network Generation via Diffusion Model—0
Non-autoregressive Conditional Diffusion Models for Time Series Prediction—0
RefineVIS: Video Instance Segmentation with Temporal Attention Refinement—0
PANE-GNN: Unifying Positive and Negative Edges in Graph Neural Networks for Recommendation—0
DEMIST: A deep-learning-based task-specific denoising approach for myocardial perfusion SPECT—0
Synthesizing realistic sand assemblies with denoising diffusion in latent space—0
Learning with Noisy Labels by Adaptive Gradient-Based Outlier RemovalCode0
Rethinking Weak Supervision in Helping Contrastive Learning—0
Phoenix: A Federated Generative Diffusion Model—0
DFormer: Diffusion-guided Transformer for Universal Image SegmentationCode1
DEK-Forecaster: A Novel Deep Learning Model Integrated with EMD-KNN for Traffic Prediction—0
Conditional Diffusion Models for Weakly Supervised Medical Image SegmentationCode1
GCD-DDPM: A Generative Change Detection Model Based on Difference-Feature Guided DDPMCode0
Compressed Sensing: A Discrete Optimization ApproachCode0
Str2Str: A Score-based Framework for Zero-shot Protein Conformation SamplingCode1
Microscopy image reconstruction with physics-informed denoising diffusion probabilistic modelCode1
PLANNER: Generating Diversified Paragraph via Latent Language Diffusion ModelCode1
On the Behavior of Intrusive and Non-intrusive Speech Enhancement Metrics in Predictive and Generative Settings—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