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 2151–2175 of 7282 papers

TitleStatusHype
DoughNet: A Visual Predictive Model for Topological Manipulation of Deformable Objects—0
G-HOP: Generative Hand-Object Prior for Interaction Reconstruction and Grasp Synthesis—0
FreeDiff: Progressive Frequency Truncation for Image Editing with Diffusion ModelsCode0
Unsupervised Microscopy Video Denoising—0
Leveraging Fine-Grained Information and Noise Decoupling for Remote Sensing Change Detection—0
Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications—0
SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing PansharpeningCode1
Factorized Diffusion: Perceptual Illusions by Noise Decomposition—0
Molecular relaxation by reverse diffusion with time step predictionCode1
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model—0
Assessing The Impact of CNN Auto Encoder-Based Image Denoising on Image Classification TasksCode0
OneActor: Consistent Character Generation via Cluster-Conditioned Guidance—0
Generating Human Interaction Motions in Scenes with Text Control—0
Digging into contrastive learning for robust depth estimation with diffusion modelsCode1
WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for Improved Feature Alignment and Local Information FusionCode1
In-Context Translation: Towards Unifying Image Recognition, Processing, and Generation—0
TMPQ-DM: Joint Timestep Reduction and Quantization Precision Selection for Efficient Diffusion Models—0
HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising—0
Masked and Shuffled Blind Spot Denoising for Real-World Images—0
RoofDiffusion: Constructing Roofs from Severely Corrupted Point Data via DiffusionCode1
Synthesis of Through-Wall Micro-Doppler Signatures of Human Motions Using Generative Adversarial Networks—0
Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction—0
NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark DatasetCode1
Lossy Image Compression with Foundation Diffusion Models—0
Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under heavy noiseCode2
Show:102550
← PrevPage 87 of 292Next →
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