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 4926–4950 of 7282 papers

TitleStatusHype
RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems—0
Riemannian Denoising Diffusion Probabilistic Models—0
Riemannian Denoising Score Matching for Molecular Structure Optimization with Accurate Energy—0
Zero-Shot Video Editing through Adaptive Sliding Score Distillation—0
RingCNN: Exploiting Algebraically-Sparse Ring Tensors for Energy-Efficient CNN-Based Computational Imaging—0
RISAR: RIS-assisted Human Activity Recognition with Commercial Wi-Fi Devices—0
Risk Estimation Without Using Stein's Lemma -- Application to Image Denoising—0
Unsupervised Learning from Noisy Networks with Applications to Hi-C Data—0
RISSOLE: Parameter-efficient Diffusion Models via Block-wise Generation and Retrieval-Guidance—0
RLCP: A Reinforcement Learning-based Copyright Protection Method for Text-to-Image Diffusion Model—0
RL Dreams: Policy Gradient Optimization for Score Distillation based 3D Generation—0
Accelerating Video Diffusion Models via Distribution Matching—0
RobGC: Towards Robust Graph Condensation—0
RoBLEURT Submission for the WMT2021 Metrics Task—0
RoBLEURT Submission for WMT2021 Metrics Task—0
Robust ADAS: Enhancing Robustness of Machine Learning-based Advanced Driver Assistance Systems for Adverse Weather—0
Robust and Computationally-Efficient Anomaly Detection using Powers-of-Two Networks—0
Robust and On-the-fly Dataset Denoising for Image Classification—0
Robust Andrew's sine estimate adaptive filtering—0
Denoising Multi-Similarity Formulation: A Self-paced Curriculum-Driven Approach for Robust Metric Learning—0
Robust CNN Multi-Nested-LSTM Framework with Compound Loss for Patch-based Multi-Push Ultrasound Shear Wave Imaging and Segmentation—0
Robust Deep Learning Ensemble against Deception—0
RobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather—0
Robust exponential binary pattern storage in Little-Hopfield networks—0
Robust Generalization Strategies for Morpheme Glossing in an Endangered Language Documentation Context—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