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 3101–3125 of 7282 papers

TitleStatusHype
Decouple-Then-Merge: Finetune Diffusion Models as Multi-Task Learning—0
Convolutional Bipartite Attractor Networks—0
Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems—0
Decouple-Then-Merge: Towards Better Training for Diffusion Models—0
Generalized linear models with low rank effects for network data—0
Convex Variational Image Restoration with Histogram Priors—0
A Self-supervised Diffusion Bridge for MRI Reconstruction—0
From Group Sparse Coding to Rank Minimization: A Novel Denoising Model for Low-level Image Restoration—0
ESPnet-se: end-to-end speech enhancement and separation toolkit designed for asr integration—0
AirLLM: Diffusion Policy-based Adaptive LoRA for Remote Fine-Tuning of LLM over the Air—0
Convex Regularization Behind Neural Reconstruction—0
A Self-Supervised Denoising Strategy for Underwater Acoustic Camera Imageries—0
HRFA: High-Resolution Feature-based Attack—0
Generate Natural Language Explanations for Recommendation—0
Error mitigation of entangled states using brainbox quantum autoencoders—0
Generating Bug-Fixes Using Pretrained Transformers—0
Deep Belief Networks for Image Denoising—0
Generating, Fast and Slow: Scalable Parallel Video Generation with Video Interface Networks—0
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion—0
Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations—0
Generating High Dimensional User-Specific Wireless Channels using Diffusion Models—0
Generating High-Resolution Regional Precipitation Using Conditional Diffusion Model—0
Convex Optimization for Parallel Energy Minimization—0
Generating Human Interaction Motions in Scenes with Text Control—0
Aerial Active STAR-RIS-assisted Satellite-Terrestrial Covert Communications—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