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 10011025 of 7282 papers

TitleStatusHype
Masked Pre-training Enables Universal Zero-shot DenoiserCode1
Are Diffusion Models Vision-And-Language Reasoners?Code1
Adversarial Distortion Learning for Medical Image DenoisingCode1
E-MLB: Multilevel Benchmark for Event-Based Camera DenoisingCode1
Are Deep Neural Architectures Losing Information? Invertibility Is IndispensableCode1
A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud CompletionCode1
CycleISP: Real Image Restoration via Improved Data SynthesisCode1
A Recycling Training Strategy for Medical Image Segmentation with Diffusion Denoising ModelsCode1
CT-Mamba: A Hybrid Convolutional State Space Model for Low-Dose CT DenoisingCode1
Burst Image Restoration and EnhancementCode1
Curriculum Disentangled Recommendation with Noisy Multi-feedbackCode1
CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy LabelsCode1
A Conditional Diffusion Model for Electrical Impedance Tomography Image ReconstructionCode1
C2N: Practical Generative Noise Modeling for Real-World DenoisingCode1
CTformer: Convolution-free Token2Token Dilated Vision Transformer for Low-dose CT DenoisingCode1
Memory AMPCode1
Cached Multi-Lora Composition for Multi-Concept Image GenerationCode1
A Neural-Network-Based Convex Regularizer for Inverse ProblemsCode1
AR-Diffusion: Auto-Regressive Diffusion Model for Text GenerationCode1
EDA-DM: Enhanced Distribution Alignment for Post-Training Quantization of Diffusion ModelsCode1
A Neural Space-Time Representation for Text-to-Image PersonalizationCode1
Enhanced Seq2Seq Autoencoder via Contrastive Learning for Abstractive Text SummarizationCode1
AR-DAE: Towards Unbiased Neural Entropy Gradient EstimationCode1
Adversarial Counterfactual Visual ExplanationsCode1
3D Shape Generation and Completion through Point-Voxel DiffusionCode1
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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