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 5451–5475 of 7282 papers

TitleStatusHype
Auto Tensor Singular Value Thresholding: A Non-Iterative and Rank-Free Framework for Tensor Denoising—0
Auxiliary Features-Guided Super Resolution for Monte Carlo Rendering—0
A Variational Approach to Shape-from-shading Under Natural Illumination—0
Variational Autoencoding Molecular Graphs with Denoising Diffusion Probabilistic Model—0
A Variational Step for Reduction of Mixed Gaussian-Impulse Noise from Images—0
Simultaneous Visual Data Completion and Denoising Based on Tensor Rank and Total Variation Minimization and Its Primal-Dual Splitting Algorithm—0
Avoiding mode collapse in diffusion models fine-tuned with reinforcement learning—0
AWCD: An Efficient Point Cloud Processing Approach via Wasserstein Curvature—0
Poisson-Gaussian Holographic Phase Retrieval with Score-based Image Prior—0
SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution—0
Backdoor Attacks against Image-to-Image Networks—0
Background Denoising for Ptychography via Wigner Distribution Deconvolution—0
Variational Coupling Revisited: Simpler Models, Theoretical Connections, and Novel Applications—0
Backpropagation with N-D Vector-Valued Neurons Using Arbitrary Bilinear Products—0
Back to Basics: Fast Denoising Iterative Algorithm—0
BADGR: Bundle Adjustment Diffusion Conditioned by GRadients for Wide-Baseline Floor Plan Reconstruction—0
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE—0
Balancing Act: Distribution-Guided Debiasing in Diffusion Models—0
Balancing the Style-Content Trade-Off in Sentiment Transfer UsingPolarity-Aware Denoising—0
BandRC: Band Shifted Raised Cosine Activated Implicit Neural Representations—0
A Blind Multiscale Spatial Regularization Framework for Kernel-based Spectral Unmixing—0
Basis Prediction Networks for Effective Burst Denoising with Large Kernels—0
Basis Pursuit Denoise with Nonsmooth Constraints—0
Basis Pursuit Denoising via Recurrent Neural Network Applied to Super-resolving SAR Tomography—0
Batch-less stochastic gradient descent for compressive learning of deep regularization 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