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 4501–4525 of 7282 papers

TitleStatusHype
Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients—0
A Design of Denser-Graph-Frequency Graph Fourier Frames for Graph Signal Analysis—0
Thermalizer: Stable autoregressive neural emulation of spatiotemporal chaos—0
Inversion-Free Video Style Transfer with Trajectory Reset Attention Control and Content-Style Bridging—0
Wasserstein Training of Boltzmann Machines—0
Investigating accuracy of pitch-accent annotations in neural network-based speech synthesis and denoising effects—0
Investigating Image Applications Based on Spatial-Frequency Transform and Deep Learning Techniques—0
Investigating Self-Supervised Image Denoising with Denaturation—0
Investigating the limited performance of a deep-learning-based SPECT denoising approach: An observer-study-based characterization—0
iPiano: Inertial Proximal Algorithm for Non-Convex Optimization—0
IPoD: Implicit Field Learning with Point Diffusion for Generalizable 3D Object Reconstruction from Single RGB-D Images—0
PanoDiffusion: 360-degree Panorama Outpainting via Diffusion—0
IPT-V2: Efficient Image Processing Transformer using Hierarchical Attentions—0
The Role of Redundant Bases and Shrinkage Functions in Image Denoising—0
The Score-Difference Flow for Implicit Generative Modeling—0
Adept: Annotation-Denoising Auxiliary Tasks with Discrete Cosine Transform Map and Keypoint for Human-Centric Pretraining—0
Ising Models with Hidden Markov Structure: Applications to Probabilistic Inference in Machine Learning—0
Islanding Detection for Active Distribution Networks Using WaveNet+UNet Classifier—0
The Silent Prompt: Initial Noise as Implicit Guidance for Goal-Driven Image Generation—0
Is Noise Conditioning Necessary? A Unified Theory of Unconditional Graph Diffusion Models—0
Is Noise Conditioning Necessary for Denoising Generative Models?—0
Iso-Diffusion: Improving Diffusion Probabilistic Models Using the Isotropy of the Additive Gaussian Noise—0
Isolating Latent Structure with Cross-population Variational Autoencoders—0
ITA-MDT: Image-Timestep-Adaptive Masked Diffusion Transformer Framework for Image-Based Virtual Try-On—0
A Denoising Diffusion Model for Fluid Field Prediction—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