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 3476–3500 of 7282 papers

TitleStatusHype
I Can See Clearly Now : Image Restoration via De-Raining—0
ICDM: Interference Cancellation Diffusion Models for Wireless Semantic Communications—0
Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models—0
Controllable Diverse Sampling for Diffusion Based Motion Behavior Forecasting—0
Controllable Confidence-Based Image Denoising—0
Identifying Arrhythmias Based on ECG Classification Using Enhanced-PCA and Enhanced-SVM Methods—0
Identifying First-order Lowpass Graph Signals using Perron Frobenius Theorem—0
Enhanced Low-Rank Matrix Approximation—0
A Robust Completed Local Binary Pattern (RCLBP) for Surface Defect Detection—0
Enhanced Denoising of Optical Coherence Tomography Images Using Residual U-Net—0
Contrast-Unity for Partially-Supervised Temporal Sentence Grounding—0
Adversarial Transferability in Deep Denoising Models: Theoretical Insights and Robustness Enhancement via Out-of-Distribution Typical Set Sampling—0
Increasing Compactness Of Deep Learning Based Speech Enhancement Models With Parameter Pruning And Quantization Techniques—0
Enhanced Denoising and Convergent Regularisation Using Tweedie Scaling—0
Beta Sampling is All You Need: Efficient Image Generation Strategy for Diffusion Models using Stepwise Spectral Analysis—0
Enhanced DACER Algorithm with High Diffusion Efficiency—0
IKDP: Inverse Kinematics through Diffusion Process—0
In-Context Translation: Towards Unifying Image Recognition, Processing, and Generation—0
Incomplete Multi-view Clustering via Diffusion Contrastive Generation—0
IlluSign: Illustrating Sign Language Videos by Leveraging the Attention Mechanism—0
Adversarial Training of Denoising Diffusion Model Using Dual Discriminators for High-Fidelity Multi-Speaker TTS—0
In-context denoising with one-layer transformers: connections between attention and associative memory retrieval—0
Incorporating Broad Phonetic Information for Speech Enhancement—0
Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics Informed Deep Autoencoders—0
Enhanced CNN for image 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