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 301325 of 7282 papers

TitleStatusHype
Deconstructing Denoising Diffusion Models for Self-Supervised LearningCode2
Graph Diffusion Transformers for Multi-Conditional Molecular GenerationCode2
Large Language Models are Efficient Learners of Noise-Robust Speech RecognitionCode2
Fixed Point Diffusion ModelsCode2
Motion2VecSets: 4D Latent Vector Set Diffusion for Non-rigid Shape Reconstruction and TrackingCode2
Attack-Resilient Image Watermarking Using Stable DiffusionCode2
FlowDiffuser: Advancing Optical Flow Estimation with Diffusion ModelsCode2
DiffLoc: Diffusion Model for Outdoor LiDAR LocalizationCode2
ZERO-IG: Zero-Shot Illumination-Guided Joint Denoising and Adaptive Enhancement for Low-Light ImagesCode2
Real-World Mobile Image Denoising Dataset with Efficient BaselinesCode2
Exposure Bracketing Is All You Need For A High-Quality ImageCode2
Z*: Zero-shot Style Transfer via Attention ReweightingCode2
FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive LearningCode2
Adaptive Guidance: Training-free Acceleration of Conditional Diffusion ModelsCode2
SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion ProcessCode2
Faster Diffusion: Rethinking the Role of the Encoder for Diffusion Model InferenceCode2
FreeInit: Bridging Initialization Gap in Video Diffusion ModelsCode2
DiAD: A Diffusion-based Framework for Multi-class Anomaly DetectionCode2
DPoser: Diffusion Model as Robust 3D Human Pose PriorCode2
DiffiT: Diffusion Vision Transformers for Image GenerationCode2
DeepCache: Accelerating Diffusion Models for FreeCode2
Flow-Guided Diffusion for Video InpaintingCode2
Diffusion360: Seamless 360 Degree Panoramic Image Generation based on Diffusion ModelsCode2
Using Human Feedback to Fine-tune Diffusion Models without Any Reward ModelCode2
Sparse4D v3: Advancing End-to-End 3D Detection and TrackingCode2
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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