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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 501–525 of 7282 papers

TitleStatusHype
PTDiffusion: Free Lunch for Generating Optical Illusion Hidden Pictures with Phase-Transferred Diffusion ModelCode1
QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-ResolutionCode1
Optimizing for the Shortest Path in Denoising Diffusion ModelCode1
DualDiff+: Dual-Branch Diffusion for High-Fidelity Video Generation with Reward GuidanceCode1
Reconciling Stochastic and Deterministic Strategies for Zero-shot Image Restoration using Diffusion Model in DualCode1
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shiftingCode1
Extrapolating and Decoupling Image-to-Video Generation Models: Motion Modeling is Easier Than You ThinkCode1
Noise-Injected Spiking Graph Convolution for Energy-Efficient 3D Point Cloud DenoisingCode1
AKDT: Adaptive Kernel Dilation Transformer for Effective Image DenoisingCode1
CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot ClassificationCode1
Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image SynthesisCode1
Reward-Guided Iterative Refinement in Diffusion Models at Test-Time with Applications to Protein and DNA DesignCode1
Classifier-free Guidance with Adaptive ScalingCode1
Diffusion Suction Grasping with Large-Scale Parcel DatasetCode1
Prompt-SID: Learning Structural Representation Prompt via Latent Diffusion for Single-Image DenoisingCode1
UniCMs: A Unified Consistency Model For Efficient Multimodal Generation and UnderstandingCode1
Cached Multi-Lora Composition for Multi-Concept Image GenerationCode1
A Mixture-Based Framework for Guiding Diffusion ModelsCode1
T-SCEND: Test-time Scalable MCTS-enhanced Diffusion ModelCode1
PM-MOE: Mixture of Experts on Private Model Parameters for Personalized Federated LearningCode1
SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion RecognitionCode1
RMDM: Radio Map Diffusion Model with Physics InformedCode1
RGB-Event ISP: The Dataset and BenchmarkCode1
Inference-Time Text-to-Video Alignment with Diffusion Latent Beam SearchCode1
RadioLLM: Introducing Large Language Model into Cognitive Radio via Hybrid Prompt and Token ReprogrammingsCode1
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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