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 251–275 of 7282 papers

TitleStatusHype
Few-Shot Concept Unlearning with Low Rank Adaptation—0
DragLoRA: Online Optimization of LoRA Adapters for Drag-based Image Editing in Diffusion ModelCode1
Video-GPT via Next Clip DiffusionCode1
dLLM-Cache: Accelerating Diffusion Large Language Models with Adaptive CachingCode3
Denoising Mutual Knowledge Distillation in Bi-Directional Multiple Instance Learning—0
Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling—0
Noisemaker 3D: Comprehensive Framework for Mesh Noise GenerationCode0
BandRC: Band Shifted Raised Cosine Activated Implicit Neural Representations—0
Attend to Not Attended: Structure-then-Detail Token Merging for Post-training DiT AccelerationCode0
Multi-Stage Speaker Diarization for Noisy ClassroomsCode0
CRISP: Clustering Multi-Vector Representations for Denoising and Pruning—0
Effective Probabilistic Time Series Forecasting with Fourier Adaptive Noise-Separated Diffusion—0
Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity—0
AW-GATCN: Adaptive Weighted Graph Attention Convolutional Network for Event Camera Data Joint Denoising and Object Recognition—0
Recent Advances in Diffusion Models for Hyperspectral Image Processing and Analysis: A Review—0
Evolutionary training-free guidance in diffusion model for 3D multi-objective molecular generation—0
DDAE++: Enhancing Diffusion Models Towards Unified Generative and Discriminative Learning—0
Prior-Guided Diffusion Planning for Offline Reinforcement Learning—0
A Fourier Space Perspective on Diffusion Models—0
Rethinking Circuit Completeness in Language Models: AND, OR, and ADDER Gates—0
ORL-LDM: Offline Reinforcement Learning Guided Latent Diffusion Model Super-Resolution Reconstruction—0
Fine-tuning Diffusion Policies with Backpropagation Through Diffusion Timesteps—0
Whitened Score Diffusion: A Structured Prior for Imaging Inverse ProblemsCode0
Robust Knowledge Graph Embedding via Denoising—0
Denoising and Alignment: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing—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