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 2951–2975 of 7282 papers

TitleStatusHype
Evolvable Conditional Diffusion—0
Evolution Meets Diffusion: Efficient Neural Architecture Generation—0
ASGDiffusion: Parallel High-Resolution Generation with Asynchronous Structure Guidance—0
A global optimization SAR image segmentation model can be easily transformed to a general ROF denoising model—0
A fast patch-dictionary method for whole image recovery—0
FlexEdit: Flexible and Controllable Diffusion-based Object-centric Image Editing—0
FlexiAct: Towards Flexible Action Control in Heterogeneous Scenarios—0
Evolutionary Variational Optimization of Generative Models—0
EventZoom: Learning To Denoise and Super Resolve Neuromorphic Events—0
Convolutional Sparse Coding with Overlapping Group Norms—0
Event-Guided Denoising for Multilingual Relation Learning—0
Flexiffusion: Segment-wise Neural Architecture Search for Flexible Denoising Schedule—0
A Set-Theoretic Study of the Relationships of Image Models and Priors for Restoration Problems—0
Geophysical inverse problems with measurement-guided diffusion models—0
Event Guided Denoising for Multilingual Relation Learning—0
EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm—0
A sensitivity analysis to quantify the impact of neuroimaging preprocessing strategies on subsequent statistical analyses—0
Convolutional Neural Network Transformer (CNNT) for Fluorescence Microscopy image Denoising with Improved Generalization and Fast Adaptation—0
FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models—0
A CT Image Denoising Method with Residual Encoder-Decoder Network—0
FlowRAM: Grounding Flow Matching Policy with Region-Aware Mamba Framework for Robotic Manipulation—0
Geometry-Aware Neighborhood Search for Learning Local Models for Image Reconstruction—0
EventDiff: A Unified and Efficient Diffusion Model Framework for Event-based Video Frame Interpolation—0
Event-Customized Image Generation—0
Convolutional Neural Networks for Spherical Signal Processing via Spherical Haar Tight Framelets—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