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 3051–3075 of 7282 papers

TitleStatusHype
Factor Adjusted Spectral Clustering for Mixture Models—0
3D Photon Counting CT Image Super-Resolution Using Conditional Diffusion Model—0
Denoising Pre-Training and Customized Prompt Learning for Efficient Multi-Behavior Sequential Recommendation—0
Pixel Is Not A Barrier: An Effective Evasion Attack for Pixel-Domain Diffusion Models—0
First line of defense: A robust first layer mitigates adversarial attacksCode0
Detection-Driven Object Count Optimization for Text-to-Image Diffusion Models—0
Denoising Plane Wave Ultrasound Images Using Diffusion Probabilistic Models—0
Hierarchical Attention Diffusion Networks with Object Priors for Video Change Detection—0
The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks—0
Instruction-Based Molecular Graph Generation with Unified Text-Graph Diffusion ModelCode0
Multi-Scale Representation Learning for Image Restoration with State-Space Model—0
Perception-based multiplicative noise removal using SDEsCode0
Modeling the Neonatal Brain Development Using Implicit Neural RepresentationsCode0
A lifted Bregman strategy for training unfolded proximal neural network Gaussian denoisers—0
MicroSSIM: Improved Structural Similarity for Comparing Microscopy DataCode0
Classifier-Free Guidance is a Predictor-Corrector—0
Diffusion Model for Planning: A Systematic Literature Review—0
Learned denoising with simulated and experimental low-dose CT data—0
Deep Joint Denoising and Detection for Enhanced Intracellular Particle Analysis—0
Cross-Modal Denoising: A Novel Training Paradigm for Enhancing Speech-Image Retrieval—0
LLM4DSR: Leveraing Large Language Model for Denoising Sequential Recommendation—0
Disentangle and denoise: Tackling context misalignment for video moment retrieval—0
Low-Bitwidth Floating Point Quantization for Efficient High-Quality Diffusion Models—0
BSS-CFFMA: Cross-Domain Feature Fusion and Multi-Attention Speech Enhancement Network based on Self-Supervised EmbeddingCode0
How to Best Combine Demosaicing and Denoising?—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