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 3301–3325 of 7282 papers

TitleStatusHype
ManiCM: Real-time 3D Diffusion Policy via Consistency Model for Robotic Manipulation—0
Convergence of the denoising diffusion probabilistic models for general noise schedules—0
Advancing Weakly-Supervised Audio-Visual Video Parsing via Segment-wise Pseudo Labeling—0
Diffusion Boosted Trees—0
Imitating the Functionality of Image-to-Image Models Using a Single Example—0
Diffusion Tuning: Transferring Diffusion Models via Chain of Forgetting—0
Knowledge Enhanced Multi-intent Transformer Network for RecommendationCode0
Bootstrap3D: Improving Multi-view Diffusion Model with Synthetic Data—0
Information Theoretic Text-to-Image Alignment—0
Unified Directly Denoising for Both Variance Preserving and Variance Exploding Diffusion Models—0
Information Maximization via Variational Autoencoders for Cross-Domain Recommendation—0
Adv-KD: Adversarial Knowledge Distillation for Faster Diffusion SamplingCode0
LED: A Large-scale Real-world Paired Dataset for Event Camera DenoisingCode0
P-MSDiff: Parallel Multi-Scale Diffusion for Remote Sensing Image SegmentationCode0
Recurrent Deep Kernel Learning of Dynamical Systems—0
Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-ClassificationCode0
On the Condition Monitoring of Bolted Joints through Acoustic Emission and Deep Transfer Learning: Generalization, Ordinal Loss and Super-Convergence—0
Going beyond Compositions, DDPMs Can Produce Zero-Shot InterpolationsCode0
Contrastive-Adversarial and Diffusion: Exploring pre-training and fine-tuning strategies for sulcal identification—0
Zero-to-Hero: Enhancing Zero-Shot Novel View Synthesis via Attention Map Filtering—0
Improving global awareness of linkset predictions using Cross-Attentive Modulation tokens—0
MixDQ: Memory-Efficient Few-Step Text-to-Image Diffusion Models with Metric-Decoupled Mixed Precision Quantization—0
VITON-DiT: Learning In-the-Wild Video Try-On from Human Dance Videos via Diffusion Transformers—0
A Refined 3D Gaussian Representation for High-Quality Dynamic Scene Reconstruction—0
Diffusion Model Patching via Mixture-of-Prompts—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