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 3376–3400 of 7282 papers

TitleStatusHype
High Noise Scheduling is a Must—0
Enhancing Label-efficient Medical Image Segmentation with Text-guided Diffusion Models—0
A Critical Analysis of Patch Similarity Based Image Denoising Algorithms—0
Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI—0
Implicit neural representations for end-to-end PET reconstruction—0
Controlled Learning of Pointwise Nonlinearities in Neural-Network-Like Architectures—0
High-Quality Real-Time Rendering Using Subpixel Sampling Reconstruction—0
High-resolution efficient image generation from WiFi CSI using a pretrained latent diffusion model—0
Enhancing Glass Defect Detection with Diffusion Models: Addressing Imbalanced Datasets in Manufacturing Quality Control—0
High-Resolution Image Synthesis via Next-Token Prediction—0
Artifact reduction for separable non-local means—0
Enhancing GANs with Contrastive Learning-Based Multistage Progressive Finetuning SNN and RL-Based External Optimization—0
Enhancing Few-Shot Stock Trend Prediction with Large Language Models—0
Controlled and Conditional Text to Image Generation with Diffusion Prior—0
Hipandas: Hyperspectral Image Joint Denoising and Super-Resolution by Image Fusion with the Panchromatic Image—0
Artefact removal in ground truth deficient fluctuations-based nanoscopy images using deep learning—0
Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements—0
HLRTF: Hierarchical Low-Rank Tensor Factorization for Inverse Problems in Multi-Dimensional Imaging—0
Controllable Satellite-to-Street-View Synthesis with Precise Pose Alignment and Zero-Shot Environmental Control—0
HOIAnimator: Generating Text-prompt Human-object Animations using Novel Perceptive Diffusion Models—0
Bayesian ECG reconstruction using denoising diffusion generative models—0
Holistic Processing of Colour Images Using Novel Quaternion-Valued Wavelets on the Plane—0
Holographic Neural Architectures—0
Homodyned K-Distribution Parameter Estimation in Quantitative Ultrasound: Autoencoder and Bayesian Neural Network Approaches—0
Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling—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