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 526–550 of 7282 papers

TitleStatusHype
A-IDE : Agent-Integrated Denoising Experts—0
Generating, Fast and Slow: Scalable Parallel Video Generation with Video Interface Networks—0
Recovering Pulse Waves from Video Using Deep Unrolling and Deep Equilibrium Models—0
Exploring the Efficacy of Partial Denoising Using Bit Plane Slicing for Enhanced Fracture Identification: A Comparative Study of Deep Learning-Based Approaches and Handcrafted Feature Extraction Techniques—0
Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising—0
Denoising-based Contractive Imitation LearningCode0
Scale-wise Distillation of Diffusion Models—0
DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup TablesCode2
BlockDance: Reuse Structurally Similar Spatio-Temporal Features to Accelerate Diffusion Transformers—0
Temporal Score Analysis for Understanding and Correcting Diffusion Artifacts—0
SceneMI: Motion In-betweening for Modeling Human-Scene Interactions—0
MiLA: Multi-view Intensive-fidelity Long-term Video Generation World Model for Autonomous DrivingCode1
ScalingNoise: Scaling Inference-Time Search for Generating Infinite Videos—0
Shining Yourself: High-Fidelity Ornaments Virtual Try-on with Diffusion Model—0
Patch-based learning of adaptive Total Variation parameter maps for blind image denoising—0
Analysis and Extension of Noisy-target Training for Unsupervised Target Signal Enhancement—0
SuperPC: A Single Diffusion Model for Point Cloud Completion, Upsampling, Denoising, and Colorization—0
MagicComp: Training-free Dual-Phase Refinement for Compositional Video Generation—0
MOSAIC: Generating Consistent, Privacy-Preserving Scenes from Multiple Depth Views in Multi-Room Environments—0
Revealing higher-order neural representations of uncertainty with the Noise Estimation through Reinforcement-based Diffusion (NERD) model—0
SIR-DIFF: Sparse Image Sets Restoration with Multi-View Diffusion Model—0
DIFFVSGG: Diffusion-Driven Online Video Scene Graph GenerationCode1
SketchFusion: Learning Universal Sketch Features through Fusing Foundation Models—0
Fundamental Limits of Matrix Sensing: Exact Asymptotics, Universality, and Applications—0
SALAD: Skeleton-aware Latent Diffusion for Text-driven Motion Generation and EditingCode2
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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