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 Added Most Hyped Most Active Needs Verification Most Verified

Showing 2311–2320 of 7282 papers

Title	Date	Tasks	Status	Score
Improving Grammatical Error Correction via Pre-Training a Copy-Augmented Architecture with Unlabeled Data	Mar 1, 2019	DenoisingGrammatical Error Correction	CodeCode Available	5
Improving Hypernymy Extraction with Distributional Semantic Classes	Nov 8, 2017	Denoising	CodeCode Available	5
Convexified Convolutional Neural Networks	Sep 4, 2016	Denoising	CodeCode Available	5
Improving Chinese Story Generation via Awareness of Syntactic Dependencies and Semantics	Oct 19, 2022	DenoisingRepresentation Learning	CodeCode Available	5
ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion Models	Nov 23, 2023	DenoisingImage Generation	CodeCode Available	5
Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex Penalties	Sep 22, 2020	DenoisingImage Denoising	CodeCode Available	5
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM	Jul 20, 2022	DenoisingFederated Learning	CodeCode Available	5
Improved Out-of-Scope Intent Classification with Dual Encoding and Threshold-based Re-Classification	May 30, 2024	ClassificationDenoising	CodeCode Available	5
Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction	May 7, 2025	compressed sensingDenoising	CodeCode Available	5
Improved Diffusion-based Generative Model with Better Adversarial Robustness	Feb 24, 2025	Adversarial RobustnessDenoising	CodeCode Available	5

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All datasets Darmstadt Noise Dataset DIV2K DND iris

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	SINDy	PSNR	81	—	Unverified
2	Pixel-shuffling Downsampling	PSNR	38.4	—	Unverified
3	TWSC	PSNR	37.93	—	Unverified
4	CBDNet(Syn)	PSNR	37.57	—	Unverified
5	MCWNNM	PSNR	37.38	—	Unverified
6	Han et al	PSNR	35.95	—	Unverified
7	FFDNet	PSNR	34.4	—	Unverified
8	TNRD	PSNR	33.65	—	Unverified
9	CDnCNN-B	PSNR	32.43	—	Unverified
10	NLRN	PSNR	30.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DRUnet_Poisson_0.01	Average PSNR (dB)	33.92	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	DRANet	Average PSNR	39.64	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PCNN+RL+HME	Average	84.61	—	Unverified