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 3001–3025 of 7282 papers

TitleStatusHype
Super Denoise Net: Speech Super Resolution with Noise Cancellation in Low Sampling Rate Noisy Environments—0
ViTs are Everywhere: A Comprehensive Study Showcasing Vision Transformers in Different Domain—0
Generative quantum machine learning via denoising diffusion probabilistic modelsCode1
Transforming Pixels into a Masterpiece: AI-Powered Art Restoration using a Novel Distributed Denoising CNN (DDCNN)—0
Image Compression and Decompression Framework Based on Latent Diffusion Model for Breast MammographyCode0
Multi-objective Progressive Clustering for Semi-supervised Domain Adaptation in Speaker Verification—0
SeeDS: Semantic Separable Diffusion Synthesizer for Zero-shot Food DetectionCode1
DiffNAS: Bootstrapping Diffusion Models by Prompting for Better Architectures—0
Observation-Guided Diffusion Probabilistic ModelsCode0
Predictive microstructure image generation using denoising diffusion probabilistic modelsCode1
RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path TracingCode0
Analysis of learning a flow-based generative model from limited sample complexityCode0
Denoising Diffusion Step-aware ModelsCode1
ACT-Net: Anchor-context Action Detection in Surgery Videos—0
Efficient Video and Audio processing with Loihi 2—0
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion ModelsCode1
Aligning Text-to-Image Diffusion Models with Reward BackpropagationCode2
A Complementary Global and Local Knowledge Network for Ultrasound denoising with Fine-grained RefinementCode0
Certification of Deep Learning Models for Medical Image SegmentationCode0
Blind CT Image Quality Assessment Using DDPM-derived Content and Transformer-based Evaluator—0
Generalization in diffusion models arises from geometry-adaptive harmonic representationsCode1
Magicremover: Tuning-free Text-guided Image inpainting with Diffusion ModelsCode0
On Memorization in Diffusion ModelsCode1
Learning to Reach Goals via DiffusionCode0
ED-NeRF: Efficient Text-Guided Editing of 3D Scene with Latent Space NeRF—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