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 1526–1550 of 7282 papers

TitleStatusHype
Reconstruction from edge image combined with color and gradient difference for industrial surface anomaly detectionCode1
LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-DiffusionCode1
LIR: A Lightweight Baseline for Image RestorationCode1
Deep learning-based denoising for fast time-resolved flame emission spectroscopy in high-pressure combustion environmentCode1
Deep learning architectural designs for super-resolution of noisy imagesCode1
AIM 2020 Challenge on Learned Image Signal Processing PipelineCode1
Decoder Denoising Pretraining for Semantic SegmentationCode1
Learning to See in the DarkCode1
Refining Generative Process with Discriminator Guidance in Score-based Diffusion ModelsCode1
CDLNet: Robust and Interpretable Denoising Through Deep Convolutional Dictionary LearningCode1
CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and DemosaicingCode1
ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion TransformerCode1
Deep Image PriorCode1
CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme ConditionsCode1
Learning to Generate Realistic LiDAR Point CloudsCode1
CCDM: Continuous Conditional Diffusion Models for Image GenerationCode1
DeepKD: A Deeply Decoupled and Denoised Knowledge Distillation TrainerCode1
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial TrainingCode1
Learning to Translate Noise for Robust Image DenoisingCode1
CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion ModelCode1
Decoupled Data Consistency with Diffusion Purification for Image RestorationCode1
Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at ScaleCode1
Learning to Discretize Denoising Diffusion ODEsCode1
Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video DenoisingCode1
Learning to Drop: Robust Graph Neural Network via Topological DenoisingCode1
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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