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 3501–3550 of 7282 papers

TitleStatusHype
Enhanced Denoising and Convergent Regularisation Using Tweedie Scaling—0
Image Blind Denoising With Generative Adversarial Network Based Noise Modeling—0
Enhanced DACER Algorithm with High Diffusion Efficiency—0
Multiscale Structure Guided Diffusion for Image Deblurring—0
InstructGIE: Towards Generalizable Image Editing—0
Image Deconvolution with Deep Image and Kernel Priors—0
Image Denoising and Inpainting with Deep Neural Networks—0
Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention—0
Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network—0
InstGenIE: Generative Image Editing Made Efficient with Mask-aware Caching and Scheduling—0
Image denoising as a conditional expectation—0
Image denoising based on improved data-driven sparse representation—0
Image Denoising by Gaussian Patch Mixture Model and Low Rank Patches—0
Image denoising by Super Neurons: Why go deep?—0
Image Denoising for Strong Gaussian Noises With Specialized CNNs for Different Frequency Components—0
Image denoising in acoustic field microscopy—0
Image Denoising in FPGA using Generic Risk Estimation—0
Image Denoising Inspired by Quantum Many-Body physics—0
Adversarial Training of Denoising Diffusion Model Using Dual Discriminators for High-Fidelity Multi-Speaker TTS—0
Image denoising through bivariate shrinkage function in framelet domain—0
Enhanced Confocal Laser Scanning Microscopy with Adaptive Physics Informed Deep Autoencoders—0
Image Denoising using Attention-Residual Convolutional Neural Networks—0
Enhanced CNN for image denoising—0
Denoising and Reconstruction of Nonlinear Dynamics using Truncated Reservoir Computing—0
Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis—0
Image denoising using group sparsity residual and external nonlocal self-similarity prior—0
Enhanced channel estimation for near-field IRS-aided multi-user MIMO system via a large deep residual network—0
Image Denoising using New Adaptive Based Median Filters—0
Image Denoising using Optimally Weighted Bilateral Filters: A Sure and Fast Approach—0
Image Denoising Using Sparsifying Transform Learning and Weighted Singular Values Minimization—0
Enhanced artificial intelligence-based diagnosis using CBCT with internal denoising: Clinical validation for discrimination of fungal ball, sinusitis, and normal cases in the maxillary sinus—0
Image Denoising via CNNs: An Adversarial Approach—0
Image Denoising via Collaborative Dual-Domain Patch Filtering—0
Image Denoising Via Collaborative Support-Agnostic Recovery—0
A Coupling Enhancement Algorithm for ZrO2 Ceramic Bearing Ball Surface Defect Detection Based on Cartoon-texture Decomposition Model and Multi-Scale Filtering Method—0
Image denoising via K-SVD with primal-dual active set algorithm—0
Integrating Unsupervised Data Generation into Self-Supervised Neural Machine Translation for Low-Resource Languages—0
Image Denoising via Style Disentanglement—0
Enhanced 3DTV Regularization and Its Applications on Hyper-spectral Image Denoising and Compressed Sensing—0
Image denoising with generalized Gaussian mixture model patch priors—0
Denoising Attention for Query-aware User Modeling in Personalized Search—0
Image Denoising with Kernels based on Natural Image Relations—0
Enhanced 3D Generation by 2D Editing—0
Image Deraining and Denoising Convolutional Neural Network ForAutonomous Driving—0
Contrastive Learning for Low-light Raw Denoising—0
Image Edge Restoring Filter—0
Denoising Autoencoders for Overgeneralization in Neural Networks—0
Image enhancement in acoustic-resolution photoacoustic microscopy enabled by a novel directional algorithm—0
Beyond Photo Realism for Domain Adaptation from Synthetic Data—0
Energy-Inspired Self-Supervised Pretraining for Vision Models—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