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 3426–3450 of 7282 papers

TitleStatusHype
Unsupervised Microscopy Video Denoising—0
Multi-Sensor Diffusion-Driven Optical Image Translation for Large-Scale Applications—0
OneActor: Consistent Character Generation via Cluster-Conditioned Guidance—0
Generating Human Interaction Motions in Scenes with Text Control—0
Assessing The Impact of CNN Auto Encoder-Based Image Denoising on Image Classification TasksCode0
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model—0
HSIDMamba: Exploring Bidirectional State-Space Models for Hyperspectral Denoising—0
Masked and Shuffled Blind Spot Denoising for Real-World Images—0
In-Context Translation: Towards Unifying Image Recognition, Processing, and Generation—0
TMPQ-DM: Joint Timestep Reduction and Quantization Precision Selection for Efficient Diffusion Models—0
Multibranch Generative Models for Multichannel Imaging with an Application to PET/CT Synergistic Reconstruction—0
Synthesis of Through-Wall Micro-Doppler Signatures of Human Motions Using Generative Adversarial Networks—0
Lossy Image Compression with Foundation Diffusion Models—0
ConsistencyDet: A Few-step Denoising Framework for Object Detection Using the Consistency ModelCode0
Single Stage Adaptive Multi-Attention Network for Image RestorationCode0
Unveiling the potential of diffusion model-based framework with transformer for hyperspectral image classification—0
GoodDrag: Towards Good Practices for Drag Editing with Diffusion Models—0
Deep Generative Sampling in the Dual Divergence Space: A Data-efficient & Interpretative Approach for Generative AI—0
Move Anything with Layered Scene Diffusion—0
Adversarial purification for no-reference image-quality metrics: applicability study and new methods—0
Efficient Denoising using Score Embedding in Score-based Diffusion Models—0
DiffusionDialog: A Diffusion Model for Diverse Dialog Generation with Latent Space—0
Quantum State Generation with Structure-Preserving Diffusion Model—0
High Noise Scheduling is a Must—0
Masked Modeling Duo: Towards a Universal Audio Pre-training Framework—0
Show:102550
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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