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 2851–2875 of 7282 papers

TitleStatusHype
Manifold Modeling in Embedded Space: A Perspective for Interpreting Deep Image PriorCode0
Restorer: Removing Multi-Degradation with All-Axis Attention and Prompt GuidanceCode0
DiffLLE: Diffusion-guided Domain Calibration for Unsupervised Low-light Image Enhancement—0
Calliffusion: Chinese Calligraphy Generation and Style Transfer with Diffusion Modeling—0
DiffKAN-Inpainting: KAN-based Diffusion model for brain tumor inpainting—0
Calibration of depth cameras using denoised depth images—0
CAGE: Controllable Articulation GEneration—0
CaDM: Codec-aware Diffusion Modeling for Neural-enhanced Video Streaming—0
A Deep Learning based Detection Method for Combined Integrity-Availability Cyber Attacks in Power System—0
DiffImp: Efficient Diffusion Model for Probabilistic Time Series Imputation with Bidirectional Mamba Backbone—0
DiffI2I: Efficient Diffusion Model for Image-to-Image Translation—0
DiffHuman: Probabilistic Photorealistic 3D Reconstruction of Humans—0
Cache Me if You Can: Accelerating Diffusion Models through Block Caching—0
DiffHand: End-to-End Hand Mesh Reconstruction via Diffusion Models—0
DiffGaze: A Diffusion Model for Continuous Gaze Sequence Generation on 360° Images—0
DiffGAP: A Lightweight Diffusion Module in Contrastive Space for Bridging Cross-Model Gap—0
A Neural Denoising Vocoder for Clean Waveform Generation from Noisy Mel-Spectrogram based on Amplitude and Phase Predictions—0
A Deep Learning Approach to Structured Signal Recovery—0
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs—0
DiffGANPaint: Fast Inpainting Using Denoising Diffusion GANs—0
DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for Structural Connectivity Prediction from Brain fMRI—0
DiffForensics: Leveraging Diffusion Prior to Image Forgery Detection and Localization—0
An Ensemble Model for Distorted Images in Real Scenarios—0
MDiff-FMT: Morphology-aware Diffusion Model for Fluorescence Molecular Tomography with Small-scale Datasets—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