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 701–710 of 7282 papers

Title	Date	Tasks	Status	Hype	Score
CoT-BERT: Enhancing Unsupervised Sentence Representation through Chain-of-Thought	Sep 20, 2023	Contrastive LearningDenoising	CodeCode Available	1	5
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction	Nov 30, 2021	DenoisingImage Reconstruction	CodeCode Available	1	5
Convergence Guarantees for Non-Convex Optimisation with Cauchy-Based Penalties	Mar 10, 2020	DenoisingImage Reconstruction	CodeCode Available	1	5
DPCSpell: A Transformer-based Detector-Purificator-Corrector Framework for Spelling Error Correction of Bangla and Resource Scarce Indic Languages	Nov 7, 2022	Bangla Spelling Error CorrectionDenoising	CodeCode Available	1	5
DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented Generation	Jun 9, 2024	Common Sense ReasoningDenoising	CodeCode Available	1	5
Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification	Nov 4, 2023	ClassificationCross-Domain Few-Shot	CodeCode Available	1	5
Domain Generalization for Object Recognition with Multi-task Autoencoders	Aug 31, 2015	DenoisingDomain Generalization	CodeCode Available	1	5
Crystal Structure Prediction by Joint Equivariant Diffusion	Jul 30, 2023	DenoisingPrediction	CodeCode Available	1	5
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels	Dec 11, 2023	DenoisingLearning with noisy labels	CodeCode Available	1	5
Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series Transformer	Jul 6, 2022	DenoisingTime Series	CodeCode Available	1	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