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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3451–3475 of 7282 papers

TitleStatusHype
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework—0
A Training-Free Plug-and-Play Watermark Framework for Stable Diffusion—0
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation—0
Wi-Fi-based Personnel Identity Recognition: Addressing Dataset Imbalance with C-DDPMs—0
Convolutional Neural Network Transformer (CNNT) for Fluorescence Microscopy image Denoising with Improved Generalization and Fast Adaptation—0
DifFUSER: Diffusion Model for Robust Multi-Sensor Fusion in 3D Object Detection and BEV Segmentation—0
Pixel-wise RL on Diffusion Models: Reinforcement Learning from Rich Feedback—0
Uformer: A UNet-Transformer fused robust end-to-end deep learning framework for real-time denoising of lung sounds—0
LCM-Lookahead for Encoder-based Text-to-Image Personalization—0
Spatio-Spectral Structure Tensor Total Variation for Hyperspectral Image Denoising and DestripingCode0
MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation—0
A Directional Diffusion Graph Transformer for Recommendation—0
On the Scalability of Diffusion-based Text-to-Image 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
Quantifying Noise of Dynamic Vision Sensor—0
A CT Image Denoising Method with Residual Encoder-Decoder Network—0
Towards Memorization-Free Diffusion Models—0
Few-shot point cloud reconstruction and denoising via learned Guassian splats renderings and fine-tuned diffusion featuresCode0
Enhancing Real-World Active Speaker Detection with Multi-Modal Extraction Pre-Training—0
Diffusion-Driven Domain Adaptation for Generating 3D Molecules—0
IPT-V2: Efficient Image Processing Transformer using Hierarchical Attentions—0
Denoising Low-dose Images Using Deep Learning of Time Series Images—0
GAMA-IR: Global Additive Multidimensional Averaging for Fast Image Restoration—0
Denoising Monte Carlo Renders with Diffusion ModelsCode0
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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