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

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Showing 3926–3950 of 7282 papers

TitleStatusHype
Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing—0
Modelling Computational Resources for Next Generation Sequencing Bioinformatics Analysis of 16S rRNA Samples—0
Modelling local phase of images and textures with applications in phase denoising and phase retrieval—0
Towards Understanding Graph Neural Networks: An Algorithm Unrolling Perspective—0
Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising—0
Modiff: Action-Conditioned 3D Motion Generation with Denoising Diffusion Probabilistic Models—0
Towards Understanding Text Hallucination of Diffusion Models via Local Generation Bias—0
A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling—0
Towards Understanding the Working Mechanism of Text-to-Image Diffusion Model—0
Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning—0
MoE-Gyro: Self-Supervised Over-Range Reconstruction and Denoising for MEMS Gyroscopes—0
Towards Unsupervised Learning based Denoising of Cyber Physical System Data to Mitigate Security Concerns—0
MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis—0
Towards Unsupervised Speech-to-Text Translation—0
Toward Theoretical Insights into Diffusion Trajectory Distillation via Operator Merging—0
MoManifold: Learning to Measure 3D Human Motion via Decoupled Joint Acceleration Manifolds—0
Moment Transform-Based Compressive Sensing in Image Processing—0
Momentum-Net for Low-Dose CT Image Reconstruction—0
Monocular 3D Object Detection with Bounding Box Denoising in 3D by Perceiver—0
Monocular Depth Estimation using Diffusion Models—0
Toward Universal Speech Enhancement for Diverse Input Conditions—0
Monotonically Convergent Regularization by Denoising—0
Adaptively Denoising Proposal Collection for Weakly Supervised Object Localization—0
Monte Carlo non local means: Random sampling for large-scale image filtering—0
Monte Carlo Tree Diffusion for System 2 Planning—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