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Denoising

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하위 태스크: 6
평가표: 6
보존된 평가 기록: 15

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.

<span style="color:grey; opacity: 0.6">( Image credit: Beyond a Gaussian Denoiser )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/jsonBackup/Latent-CLIP-Demo

공식 코드

Controlling Latent Diffusion Using Latent CLIP

https://github.com/jsshin98/ssgnet

공식 코드

Task-specific Scene Structure Representations

https://github.com/jsvir/vad

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SG-VAD: Stochastic Gates Based Speech Activity Detection

https://github.com/juglab/DecoNoising

공식 코드

Improving Blind Spot Denoising for Microscopy

https://github.com/juglab/DenoiSeg

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DenoiSeg: Joint Denoising and Segmentation

https://github.com/juglab/VoidSeg

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Leveraging Self-supervised Denoising for Image Segmentation

https://github.com/juglab/microssim

공식 코드

MicroSSIM: Improved Structural Similarity for Comparing Microscopy Data

https://github.com/juglab/ppn2v

공식 코드

Fully Unsupervised Probabilistic Noise2Void

https://github.com/juliawolleb/anomaly_berdiff

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Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly Detection

https://github.com/junaidmalik09/rwdenoise

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Convolutional versus Self-Organized Operational Neural Networks for Real-World Blind Image Denoising

https://github.com/junhuihe2000/exampleebars

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Adaptive Bayesian Multivariate Spline Knot Inference with Prior Specifications on Model Complexity

https://github.com/junoh-kang/ogdm_edm

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Observation-Guided Diffusion Probabilistic Models

https://github.com/junyuchen245/Active-Contour-Wavelet-Seg

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Feature-Based Image Clustering and Segmentation Using Wavelets

https://github.com/junzhuang-code/lindt

공식 코드

Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation

https://github.com/justinle4/diffusion

공식 코드

Generative Modeling with Diffusion

https://github.com/justinpinkney/clip2latent

공식 코드

clip2latent: Text driven sampling of a pre-trained StyleGAN using denoising diffusion and CLIP

https://github.com/jwen307/quality_uq

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Conformal Bounds on Full-Reference Image Quality for Imaging Inverse Problems

https://github.com/jychoi118/ilvr_adm

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ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models

https://github.com/jychoi118/p2-weighting

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Perception Prioritized Training of Diffusion Models

https://github.com/jychoi118/toward_spatial_unbiased

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Toward Spatially Unbiased Generative Models

https://github.com/jzsherlock4869/denoise-ignet

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Exploring Inter-frequency Guidance of Image for Lightweight Gaussian Denoising

https://github.com/k-radlak/IDCNN

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Deep Learning based Switching Filter for Impulsive Noise Removal in Color Images

https://github.com/kagaminccino/LAVSE

공식 코드

Lite Audio-Visual Speech Enhancement

https://github.com/kagawa588/diffvsgg

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DIFFVSGG: Diffusion-Driven Online Video Scene Graph Generation

https://github.com/kaike-zhang/pld

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Personalized Denoising Implicit Feedback for Robust Recommender System

https://github.com/kaiyangshi-ito/nonconvexag

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Accelerated Gradient Methods for Sparse Statistical Learning with Nonconvex Penalties

https://github.com/kalmannet/hkf_icassp23

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HKF: Hierarchical Kalman Filtering with Online Learned Evolution Priors for Adaptive ECG Denoising

https://github.com/kamildeja/analysing_ddgm

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On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models

https://github.com/kangliao929/noise-da

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Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration

https://github.com/kantamasuki/rgdm

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Generative diffusion model with inverse renormalization group flows

https://github.com/karlisfre/diffusion-factorization

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Discrete Denoising Diffusion Approach to Integer Factorization

https://github.com/karlnapf/nystrom-kexpfam

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Efficient and principled score estimation with Nyström kernel exponential families

https://github.com/karlyukang/low-resource-multimodal-pre-training

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Self-Supervised Audio-and-Text Pre-training with Extremely Low-Resource Parallel Data

https://github.com/kaushal0494/ZmBART

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ZmBART: An Unsupervised Cross-lingual Transfer Framework for Language Generation

https://github.com/kayzliu/godm

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Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion Models

https://github.com/kbui1993/L1mL2Segmentation

공식 코드

A Weighted Difference of Anisotropic and Isotropic Total Variation for Relaxed Mumford-Shah Color and Multiphase Image Segmentation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.