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 901–925 of 7282 papers

TitleStatusHype
Text-Driven Fashion Image Editing with Compositional Concept Learning and Counterfactual Abduction—0
PDFactor: Learning Tri-Perspective View Policy Diffusion Field for Multi-Task Robotic Manipulation—0
Satellite to GroundScape - Large-scale Consistent Ground View Generation from Satellite Views—0
HomoGen: Enhanced Video Inpainting via Homography Propagation and Diffusion—0
Scaling Inference Time Compute for Diffusion Models—0
Multi-Modal Contrastive Masked Autoencoders: A Two-Stage Progressive Pre-training Approach for RGBD Datasets—0
STINR: Deciphering Spatial Transcriptomics via Implicit Neural Representation—0
Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning—0
Hierarchical Flow Diffusion for Efficient Frame Interpolation—0
V2X-R: Cooperative LiDAR-4D Radar Fusion with Denoising Diffusion for 3D Object Detection—0
Beyond Human Perception: Understanding Multi-Object World from Monocular ViewCode0
Beyond Generation: A Diffusion-based Low-level Feature Extractor for Detecting AI-generated Images—0
FDS: Frequency-Aware Denoising Score for Text-Guided Latent Diffusion Image Editing—0
RaSS: Improving Denoising Diffusion Samplers with Reinforced Active Sampling Scheduler—0
Deterministic Image-to-Image Translation via Denoising Brownian Bridge Models with Dual ApproximatorsCode1
ReDiffDet: Rotation-equivariant Diffusion Model for Oriented Object Detection—0
Layered Motion Fusion: Lifting Motion Segmentation to 3D in Egocentric Videos—0
VODiff: Controlling Object Visibility Order in Text-to-Image Generation—0
T2ICount: Enhancing Cross-modal Understanding for Zero-Shot CountingCode1
APT: Adaptive Personalized Training for Diffusion Models with Limited Data—0
LITA-GS: Illumination-Agnostic Novel View Synthesis via Reference-Free 3D Gaussian Splatting and Physical Priors—0
Diffusion Bridge: Leveraging Diffusion Model to Reduce the Modality Gap Between Text and Vision for Zero-Shot Image CaptioningCode1
UHD-processer: Unified UHD Image Restoration with Progressive Frequency Learning and Degradation-aware PromptsCode1
Decouple-Then-Merge: Finetune Diffusion Models as Multi-Task Learning—0
SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generation—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