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

Showing 27012725 of 7282 papers

TitleStatusHype
GanLM: Encoder-Decoder Pre-training with an Auxiliary DiscriminatorCode0
Gated Orthogonal Recurrent Units: On Learning to ForgetCode0
A Dictionary Based Approach for Removing Out-of-Focus BlurCode0
Game Theory for Adversarial Attacks and DefensesCode0
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy ImagesCode0
Gated Texture CNN for Efficient and Configurable Image DenoisingCode0
Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot LearningCode0
Fully Convolutional Network with Multi-Step Reinforcement Learning for Image ProcessingCode0
ADFormer: Aggregation Differential Transformer for Passenger Demand ForecastingCode0
Fully Adaptive Time-Varying Wave-Shape Model: Applications in Biomedical Signal ProcessingCode0
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion ModelsCode0
From Shortcuts to Triggers: Backdoor Defense with Denoised PoECode0
Diffusion Model for Camouflaged Object DetectionCode0
Fully Unsupervised Probabilistic Noise2VoidCode0
From neural PCA to deep unsupervised learningCode0
NCP: Neural Correspondence Prior for Effective Unsupervised Shape MatchingCode0
From Bag of Sentences to Document: Distantly Supervised Relation Extraction via Machine Reading ComprehensionCode0
From Patches to Images: A Nonparametric Generative ModelCode0
Fuse Your Latents: Video Editing with Multi-source Latent Diffusion ModelsCode0
CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image DenoisingCode0
Foundation Models For Seismic Data Processing: An Extensive ReviewCode0
FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural NetworksCode0
FreeDiff: Progressive Frequency Truncation for Image Editing with Diffusion ModelsCode0
FOCNet: A Fractional Optimal Control Network for Image DenoisingCode0
Focusing on what to decode and what to train: SOV Decoding with Specific Target Guided DeNoising and Vision Language AdvisorCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SINDyPSNR81Unverified
2Pixel-shuffling DownsamplingPSNR38.4Unverified
3TWSCPSNR37.93Unverified
4CBDNet(Syn)PSNR37.57Unverified
5MCWNNMPSNR37.38Unverified
6Han et alPSNR35.95Unverified
7FFDNetPSNR34.4Unverified
8TNRDPSNR33.65Unverified
9CDnCNN-BPSNR32.43Unverified
10NLRNPSNR30.8Unverified
#ModelMetricClaimedVerifiedStatus
1DRUnet_Poisson_0.01Average PSNR (dB)33.92Unverified
#ModelMetricClaimedVerifiedStatus
1DRANetAverage PSNR39.64Unverified
#ModelMetricClaimedVerifiedStatus
1PCNN+RL+HMEAverage84.61Unverified