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

Showing 21762200 of 7282 papers

TitleStatusHype
Language-Guided Diffusion Model for Visual GroundingCode0
Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation DatasetCode0
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source SeparationCode0
DCANet: Dual Convolutional Neural Network with Attention for Image Blind DenoisingCode0
Joint Visual Denoising and Classification using Deep LearningCode0
Dataset Distillers Are Good Label Denoisers In the WildCode0
Joint inference and input optimization in equilibrium networksCode0
Joint Multi-Scale Tone Mapping and Denoising for HDR Image EnhancementCode0
Data-driven Thresholding in Denoising with Spectral Graph Wavelet TransformCode0
Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal CellsCode0
Data-Driven Priors in the Maximum Entropy on the Mean Method for Linear Inverse ProblemsCode0
Data-driven modeling of time-domain induced polarizationCode0
Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel DataCode0
Attend to Not Attended: Structure-then-Detail Token Merging for Post-training DiT AccelerationCode0
Joint Demosaicking and Denoising by Fine-Tuning of Bursts of Raw ImagesCode0
A Heat Diffusion Perspective on Geodesic Preserving Dimensionality ReductionCode0
Joint Enhancement and Denoising Method via Sequential DecompositionCode0
KADEL: Knowledge-Aware Denoising Learning for Commit Message GenerationCode0
Data-Aware Training Quality Monitoring and Certification for Reliable Deep LearningCode0
Iterative Residual CNNs for Burst Photography ApplicationsCode0
Iterative PET Image Reconstruction Using Convolutional Neural Network RepresentationCode0
Iterative Learning for Joint Image Denoising and Motion Artifact Correction of 3D Brain MRICode0
Iterative Joint Image Demosaicking and Denoising using a Residual Denoising NetworkCode0
Iterative Camera-LiDAR Extrinsic Optimization via Surrogate DiffusionCode0
DARK: Denoising, Amplification, Restoration KitCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified