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 6576–6600 of 7282 papers

TitleStatusHype
A lifted Bregman strategy for training unfolded proximal neural network Gaussian denoisers—0
Diffusion Recommender Models and the Illusion of Progress: A Concerning Study of Reproducibility and a Conceptual Mismatch—0
Diffusion Reconstruction of Ultrasound Images with Informative Uncertainty—0
DiffusionRIR: Room Impulse Response Interpolation using Diffusion Models—0
Diffusion Schrödinger Bridge Matching—0
DiffusionSfM: Predicting Structure and Motion via Ray Origin and Endpoint Diffusion—0
Fast and Accurate: Video Enhancement using Sparse Depth—0
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser—0
Diffusion State Distances: Multitemporal Analysis, Fast Algorithms, and Applications to Biological Networks—0
Diffusion-Stego: Training-free Diffusion Generative Steganography via Message Projection—0
VidMan: Exploiting Implicit Dynamics from Video Diffusion Model for Effective Robot Manipulation—0
Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Models—0
Algorithm Unrolling-based Denoising of Multimodal Graph Signals—0
Algorithm-Induced Prior for Image Restoration—0
DiffusionTrend: A Minimalist Approach to Virtual Fashion Try-On—0
Diffusion Tuning: Transferring Diffusion Models via Chain of Forgetting—0
Stochastic Super-resolution of Cosmological Simulations with Denoising Diffusion Models—0
DiffusionVMR: Diffusion Model for Joint Video Moment Retrieval and Highlight Detection—0
Stochastic Texture Filtering—0
Stochastic Variational Inference with Gradient Linearization—0
DiffuVST: Narrating Fictional Scenes with Global-History-Guided Denoising Models—0
DiffuX2CT: Diffusion Learning to Reconstruct CT Images from Biplanar X-Rays—0
DiffVein: A Unified Diffusion Network for Finger Vein Segmentation and Authentication—0
Stop-and-go wave super-resolution reconstruction via iterative refinement—0
DiffYOLO: Object Detection for Anti-Noise via YOLO and Diffusion Models—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