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

Showing 351375 of 7282 papers

TitleStatusHype
D-AR: Diffusion via Autoregressive ModelsCode2
CMGAN: Conformer-Based Metric-GAN for Monaural Speech EnhancementCode2
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image GenerationCode2
Iterated Denoising Energy Matching for Sampling from Boltzmann DensitiesCode2
Large Language Models are Efficient Learners of Noise-Robust Speech RecognitionCode2
Aligning Text-to-Image Diffusion Models with Reward BackpropagationCode2
Leapfrog Diffusion Model for Stochastic Trajectory PredictionCode2
Learning-to-Cache: Accelerating Diffusion Transformer via Layer CachingCode2
Diffusion-based Visual Anagram as Multi-task LearningCode2
Spatio-Temporal Few-Shot Learning via Diffusive Neural Network GenerationCode2
DiffusionDepth: Diffusion Denoising Approach for Monocular Depth EstimationCode2
DDP: Diffusion Model for Dense Visual PredictionCode2
Diffusion360: Seamless 360 Degree Panoramic Image Generation based on Diffusion ModelsCode2
BAMM: Bidirectional Autoregressive Motion ModelCode2
Diffusion^2: Dynamic 3D Content Generation via Score Composition of Video and Multi-view Diffusion ModelsCode2
A Geometric Perspective on Diffusion ModelsCode2
DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly DetectionCode2
DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits AnimationCode2
AdaPoinTr: Diverse Point Cloud Completion with Adaptive Geometry-Aware TransformersCode2
MaIR: A Locality- and Continuity-Preserving Mamba for Image RestorationCode2
CM-TTS: Enhancing Real Time Text-to-Speech Synthesis Efficiency through Weighted Samplers and Consistency ModelsCode2
Diffusion-based Generation, Optimization, and Planning in 3D ScenesCode2
Mask2Map: Vectorized HD Map Construction Using Bird's Eye View Segmentation MasksCode2
MCVD: Masked Conditional Video Diffusion for Prediction, Generation, and InterpolationCode2
DiffIR: Efficient Diffusion Model for Image RestorationCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified