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
BOOSTING ENCODER-DECODER CNN FOR INVERSE PROBLEMS—0
An Approach for Reducing Outliers of Non Local Means Image Denoising Filter—0
A Data-Driven Paradigm-Based Image Denoising and Mosaicking Approach for High-Resolution Acoustic Camera—0
Accelerating Mobile Edge Generation (MEG) by Constrained Learning—0
Dynamic Point Cloud Denoising via Manifold-to-Manifold Distance—0
MDT-A2G: Exploring Masked Diffusion Transformers for Co-Speech Gesture Generation—0
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks—0
Denoising Time Cycle Modeling for Recommendation—0
Denoising Text to Speech with Frame-Level Noise Modeling—0
Boosting Diffusion Models with Moving Average Sampling in Frequency Domain—0
Denoising Task Difficulty-based Curriculum for Training Diffusion Models—0
Magnetic Resonance Spectroscopy Deep Learning Denoising Using Few In Vivo Data—0
Denoising single images by feature ensemble revisited—0
BM3D vs 2-Layer ONN—0
An Analysis on Quantizing Diffusion Transformers—0
A data-driven modular architecture with denoising autoencoders for health indicator construction in a manufacturing process—0
Denoising Simulated Low-Field MRI (70mT) using Denoising Autoencoders (DAE) and Cycle-Consistent Generative Adversarial Networks (Cycle-GAN)—0
Denoising Signals in Cognitive Radio Systems Using An Evolutionary Algorithm Based Adaptive Filter—0
Denoising Self-attentive Sequential Recommendation—0
Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review—0
Blurring Diffusion Models—0
Denoising Score Matching with Random Fourier Features—0
Blockwise SURE Shrinkage for Non-Local Means—0
Denoising Score Distillation: From Noisy Diffusion Pretraining to One-Step High-Quality Generation—0
Denoising Reuse: Exploiting Inter-frame Motion Consistency for Efficient Video Latent Generation—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