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 1726–1750 of 7282 papers

TitleStatusHype
Step-level Reward for Free in RL-based T2I Diffusion Model Fine-tuningCode1
Diffusion-Based Hierarchical Multi-Label Object Detection to Analyze Panoramic Dental X-raysCode1
ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial DefenseCode1
Calliffusion: Chinese Calligraphy Generation and Style Transfer with Diffusion Modeling—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
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network—0
Cache Me if You Can: Accelerating Diffusion Models through Block Caching—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
DiffSED: Sound Event Detection with Denoising Diffusion—0
An Ensemble Model for Distorted Images in Real Scenarios—0
DiffRoad: Realistic and Diverse Road Scenario Generation for Autonomous Vehicle Testing—0
A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images—0
DiffSal: Joint Audio and Video Learning for Diffusion Saliency Prediction—0
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content—0
A Deep Learning Approach to Data-driven Parameterizations for Statistical Parametric Speech Synthesis—0
Burst Denoising via Temporally Shifted Wavelet Transforms—0
ACCORD: Alleviating Concept Coupling through Dependence Regularization for Text-to-Image Diffusion Personalization—0
Burger: Robust Graph Denoising-augmentation Fusion and Multi-semantic Modeling in Social Recommendation—0
A Deep Generative Model for the Simulation of Discrete Karst Networks—0
BuilDiff: 3D Building Shape Generation using Single-Image Conditional Point Cloud Diffusion Models—0
DiffRaman: A Conditional Latent Denoising Diffusion Probabilistic Model for Bacterial Raman Spectroscopy Identification Under Limited Data Conditions—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