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 2201–2225 of 7282 papers

TitleStatusHype
DifFUSER: Diffusion Model for Robust Multi-Sensor Fusion in 3D Object Detection and BEV Segmentation—0
Latent-based Diffusion Model for Long-tailed RecognitionCode1
Uformer: A UNet-Transformer fused robust end-to-end deep learning framework for real-time denoising of lung sounds—0
Pixel-wise RL on Diffusion Models: Reinforcement Learning from Rich Feedback—0
Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and DestripingCode0
DiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR ImagesCode2
Real-time Noise Source Estimation of a Camera System from an Image and MetadataCode1
LCM-Lookahead for Encoder-based Text-to-Image Personalization—0
A Directional Diffusion Graph Transformer for Recommendation—0
MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation—0
Imaging transformer for MRI denoising with the SNR unit training: enabling generalization across field-strengths, imaging contrasts, and anatomyCode0
Solar synthetic imaging: Introducing denoising diffusion probabilistic models on SDO/AIA data—0
On the Scalability of Diffusion-based Text-to-Image Generation—0
OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning DenoisingCode1
A CT Image Denoising Method with Residual Encoder-Decoder Network—0
Quantifying Noise of Dynamic Vision Sensor—0
Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image RestorationCode2
WcDT: World-centric Diffusion Transformer for Traffic Scene GenerationCode1
Diffusion^2: Dynamic 3D Content Generation via Score Composition of Video and Multi-view Diffusion ModelsCode2
Enhancing Real-World Active Speaker Detection with Multi-Modal Extraction Pre-Training—0
Diffusion-Driven Domain Adaptation for Generating 3D Molecules—0
Towards Memorization-Free Diffusion Models—0
Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-OnCode2
Drag Your Noise: Interactive Point-based Editing via Diffusion Semantic PropagationCode2
Few-shot point cloud reconstruction and denoising via learned Guassian splats renderings and fine-tuned diffusion featuresCode0
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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