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 4451–4475 of 7282 papers

TitleStatusHype
Infusing Learned Priors into Model-Based Multispectral Imaging—0
InFusion: Inject and Attention Fusion for Multi Concept Zero-Shot Text-based Video Editing—0
Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor—0
Inhomogeneous graph trend filtering via a l2,0 cardinality penalty—0
Innovating Real Fisheye Image Correction with Dual Diffusion Architecture—0
InpDiffusion: Image Inpainting Localization via Conditional Diffusion Models—0
In-Place Scene Labelling and Understanding with Implicit Scene Representation—0
The local low-dimensionality of natural images—0
WARP-LCA: Efficient Convolutional Sparse Coding with Locally Competitive Algorithm—0
Input Snapshots Fusion for Scalable Discrete Dynamic Graph Nerual Networks—0
Insertion Language Models: Sequence Generation with Arbitrary-Position Insertions—0
Insights into analysis operator learning: From patch-based sparse models to higher-order MRFs—0
Instance Map based Image Synthesis with a Denoising Generative Adversarial Network—0
InstGenIE: Generative Image Editing Made Efficient with Mask-aware Caching and Scheduling—0
Theoretical Perspectives on Deep Learning Methods in Inverse Problems—0
InstructGIE: Towards Generalizable Image Editing—0
ADG: Ambient Diffusion-Guided Dataset Recovery for Corruption-Robust Offline Reinforcement Learning—0
Integrating Amortized Inference with Diffusion Models for Learning Clean Distribution from Corrupted Images—0
Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages—0
Integrating Vehicle Acoustic Data for Enhanced Urban Traffic Management: A Study on Speed Classification in Suzhou—0
Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes—0
Theory on Score-Mismatched Diffusion Models and Zero-Shot Conditional Samplers—0
Intention-aware Denoising Diffusion Model for Trajectory Prediction—0
Interaction Dataset of Autonomous Vehicles with Traffic Lights and Signs—0
Interactive Video Generation via Domain Adaptation—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