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 2451–2475 of 7282 papers

TitleStatusHype
Exploiting the Exact Denoising Posterior Score in Training-Free Guidance of Diffusion Models—0
Exploring Attribute Variations in Style-based GANs using Diffusion Models—0
ALA: Naturalness-aware Adversarial Lightness Attack—0
Autoencoders as Weight Initialization of Deep Classification Networks for Cancer versus Cancer Studies—0
Deep Camera: A Fully Convolutional Neural Network for Image Signal Processing—0
Explainable Synthetic Image Detection through Diffusion Timestep Ensembling—0
Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data—0
AutoDenoise: Automatic Data Instance Denoising for Recommendations—0
Adaptive Cohen's Class Time-Frequency Distribution—0
DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders—0
Deep Boosting for Image Denoising—0
Explainable Deep Learning Framework for SERS Bio-quantification—0
Deep Belief Networks for Image Denoising—0
DeepBeat: A multi-task deep learning approach to assess signal quality and arrhythmia detection in wearable devices—0
AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoising—0
Deep BCD-Net Using Identical Encoding-Decoding CNN Structures for Iterative Image Recovery—0
A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images—0
AI Security for Geoscience and Remote Sensing: Challenges and Future Trends—0
AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air—0
Adaptive Caching for Faster Video Generation with Diffusion Transformers—0
Decrypting the temperature field in flow boiling with latent diffusion models—0
Decouple-Then-Merge: Towards Better Training for Diffusion Models—0
A Unified View Between Tensor Hypergraph Neural Networks And Signal Denoising—0
2nd Place Solutions for UG2+ Challenge 2022 -- D^3Net for Mitigating Atmospheric Turbulence from Images—0
Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising—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