Papers Image Denoising
“Image Denoising” 태그가 달린 논문 1,318편 · 필터 해제
Structured Residual Connectivity Matters for Diffusion Transformers
Diffusion Transformers (DiTs) have established themselves as a scalable backbone for high-fidelity image synthesis. However, unlike U-Net based diffusion models that rely on rigid, hand-crafted skip connections, DiTs pre…
Image DenoisingEfficient 3D Whole-Body PET Image Denoising via Conditional Rectified Flow With Optimized Sampling Strategy
Reducing radiation exposure in Positron Emission Tomography (PET) is important for patient safety; however, ultra-low-dose imaging suffers from severe noise, which may affect diagnostic interpretation without appropriate…
Computational EfficiencyImage EnhancementImage DenoisingTT-net: Quantum Inspired Tensor Network Denoising in Conditional GANs
Developed as a workhorse for classical simulations of quantum algorithms and quantum many-body systems, Tensor Network methods have entered the scientific mainstream in quantum physics. Among various types of tensor netw…
Image DenoisingVLM- and LLM-Driven Multi-Agent System for PET Image Denoising
Positron emission tomography (PET) imaging suffers from limited spatial resolution and low signal-to-noise ratio, which can compromise quantitative accuracy and lesion detectability. Deep learning-based denoising methods…
Image DenoisingLiteKD-Net: Lightweight Knowledge-Distilled Network for Mobile Image Denoising
Mobile image denoising requires both good restoration quality and low computational cost. In addition, it's annoying to collect large-scale LQ-GT clean pairs. As a result, we propose LiteKD-Net, a lightweight knowledge-d…
Computational EfficiencyKnowledge DistillationImage DenoisingYeTI: You Only Need Two Noisy Images for Real-World sRGB Noise Generation
Real-world sRGB image denoising remains challenging due to the nonlinear characteristics of sensor noise and the difficulty of acquiring aligned clean-noisy image pairs. Supervised denoisers often overfit to limited pair…
Image DenoisingBridging Interleaved Multi-Modal Reasoning as a Unified Decision Process
Unified multi-modal models (UMMs) have shown promising interleaved text-image reasoning capabilities, yet effectively optimizing such multi-turn generation via reinforcement learning (RL) remains an open challenge. Exist…
Reinforcement LearningSpatial ReasoningImage GenerationImage DenoisingScaLe-INR: Scale and Learn Implicit Neural Representations
Implicit Neural Representations (INRs) parameterized by multilayer perceptrons excel at modeling continuous signals. However, a key challenge persists as INRs fundamentally suffer from spectral bias and information cross…
Image Reconstruction3D ReconstructionImage DenoisingScalable Operator Learning via Nyström Approximation With Denoising Applications
In this paper, we study Nyström subsampling for vector-valued regression in vector-valued reproducing kernel Hilbert spaces. Standard kernel methods often suffer from prohibitive computational costs due to the constructi…
Image DenoisingAudio DenoisingVSANet: View-aware Sparse Attention Network for Light Field Image Denoising
Light field (LF) image denoising is challenging due to the high-dimensional structure of LF data. While noise is independent across sub-aperture images, scene content exhibits strong cross-view correlations. We introduce…
Image DenoisingPoisson2Gaussian: Noise Gaussianization to Enhance Image Denoising
The quantum nature of light determines the inherent Poisson stochasticity of photon detection, which is ubiquitous in photography, microscopy, and astronomy. However, our controlled numerical studies reveal that the sign…
Image DenoisingTIDY: Thermal Infrared Image Denoising via Wavelet Domain Entropy and Directional Stripe Index
Thermal infrared (TIR) imaging has been a popular choice for field robotics due to its robust perception capability under low light visual degradation, but it suffers from severe stochastic and fixed-pattern noise that b…
Monocular Depth EstimationImage DenoisingThe Third Challenge on Image Denoising at NTIRE 2026: Methods and Results
This paper reports on the NTIRE 2026 Challenge on Image Denoising, specifically focusing on the high-noise regime ($σ= 50$). The competition investigates advanced neural architectures designed to restore high-fidelity de…
Image RestorationImage DenoisingU-TTT: Towards Generalizable PET Image Denoising via Test-Time Training
Existing deep learning models for Positron Emission Tomography (PET) image denoising often suffer from severe performance degradation under distribution shifts, fundamentally restricting their robust clinical deployment.…
Domain AdaptationImage DenoisingUniPET: a universal network for high-quality PET image denoising across varied dose reduction factors
Most existing deep learning-based PET image denoising methods assume a fixed and known dose reduction factor (DRF) for low-dose PET images. However, these methods encounter significant performance degradation when the DR…
Domain GeneralizationImage DenoisingPhase-Aware Wavelet-Based-Scattering Encoder-Decoder for Dense Predictions
Scattering transforms achieve Lipschitz stability and translation invariance, but dense prediction tasks require preserving spatial structure lost in global averaging. We propose Phase-Aware Scattering Encoder-Decoder, w…
Skin Lesion SegmentationImage DenoisingOracle Supervision Transfers for Hyperparameter Prediction in Model-Based Image Denoising
Hyperparameter prediction is a critical practical bottleneck for model-based image denoisers, ranging from classical TV/TGV variational solvers to modern diffusion-based models such as DiffPIR. While existing learned pre…
Image DenoisingA Mixture Autoregressive Image Generative Model on Quadtree Regions for Gaussian Noise Removal via Variational Bayes and Gradient Methods
This paper addresses the problem of image denoising for grayscale images. We propose a probabilistic image generative model that combines a quadtree region-partitioning model with a mixture autoregressive model, and prop…
Image DenoisingPyramid Self-Contrastive Learning for Single-shot Test-time Ultrasound Image Denoising
The inherent electronic and speckle noise complicates clinical interpretation of ultrasound images. Conventional denoising methods rely on explicit noise assumptions whose validity diminishes under composite noise condit…
Contrastive LearningImage DenoisingPre-training Enables Extraordinary All-optical Image Denoising
Optical neural networks are emerging as powerful machine learning and information processing tools because of their potential advantages in speed and energy efficiency. The training methods of these physical models, howe…
Transfer LearningImage DenoisingFace Detection