Papers with Code 아카이브

Denoising

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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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/thunlp/unifiedinstructiontuning

공식 코드

Exploring Format Consistency for Instruction Tuning

https://github.com/tiangexiang/BiO-Net

공식 코드

BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecture

https://github.com/tiankaihang/min-snr-diffusion-training

공식 코드

Efficient Diffusion Training via Min-SNR Weighting Strategy

https://github.com/tianxingwu/freeinit

공식 코드

FreeInit: Bridging Initialization Gap in Video Diffusion Models

https://github.com/tianyucodings/diffusion_trusted_q_learning

공식 코드

Diffusion Policies creating a Trust Region for Offline Reinforcement Learning

https://github.com/tids-lab/its

공식 코드

Boosting of Implicit Neural Representation-based Image Denoiser

https://github.com/timothygebhard/hsr4hci

공식 코드

Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal framework

https://github.com/tingz123/fha-kitchens

공식 코드

Multi-Granularity Hand Action Detection

https://github.com/tirer-lab/ddpg

공식 코드

Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned Guidance

https://github.com/tl-qz/trajdiffuse

공식 코드

TrajDiffuse: A Conditional Diffusion Model for Environment-Aware Trajectory Prediction

https://github.com/tmaham/DS-Fusion

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DS-Fusion: Artistic Typography via Discriminated and Stylized Diffusion

https://github.com/tmilne5/trust-the-critics

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Trust the Critics: Generatorless and Multipurpose WGANs with Initial Convergence Guarantees

https://github.com/tml-tuebingen/pags

공식 코드

Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness

https://github.com/tmliang/CGRE

공식 코드

Distantly-Supervised Long-Tailed Relation Extraction Using Constraint Graphs

https://github.com/tmllab/2024_icml_dicl

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Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning

https://github.com/tmllab/machine_vision_therapy

공식 코드

Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning

https://github.com/tmlr-group/noisediffusion

공식 코드

NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation

https://github.com/tmlr-group/noisyrationales

공식 코드

Can Language Models Perform Robust Reasoning in Chain-of-thought Prompting with Noisy Rationales?

https://github.com/tmlr-group/rgib

공식 코드

Combating Bilateral Edge Noise for Robust Link Prediction

https://github.com/tmlresearchgroup-cas/clipure

공식 코드

CLIPure: Purification in Latent Space via CLIP for Adversarially Robust Zero-Shot Classification

https://github.com/tntek/source-free-domain-adaptation

공식 코드

Proxy Denoising for Source-Free Domain Adaptation

https://github.com/tomato1mule/diffusion_edf

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Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulation

https://github.com/tommoral/dicodile

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Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and Signals

https://github.com/tomtirer/IDBP

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Image Restoration by Iterative Denoising and Backward Projections

https://github.com/tomtirer/IDBP-CNN-IA

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Super-Resolution via Image-Adapted Denoising CNNs: Incorporating External and Internal Learning

https://github.com/tonghelen/JED-Method

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Joint Enhancement and Denoising Method via Sequential Decomposition

https://github.com/tongyuantongyu/vs-nnvisr

공식 코드

NNVISR: Bring Neural Network Video Interpolation and Super Resolution into Video Processing Framework

https://github.com/tonyauyeung/BNEM

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BNEM: A Boltzmann Sampler Based on Bootstrapped Noised Energy Matching

https://github.com/tonyckc/RIDnet_demo

공식 코드

GCN-MIF: Graph Convolutional Network with Multi-Information Fusion for Low-dose CT Denoising

https://github.com/tonyckc/gcn-mif_demo

공식 코드

GCN-MIF: Graph Convolutional Network with Multi-Information Fusion for Low-dose CT Denoising

https://github.com/tonyliu0910/msemg

공식 코드

MSEMG: Surface Electromyography Denoising with a Mamba-based Efficient Network

https://github.com/tonyliu0910/sdemg

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SDEMG: Score-based Diffusion Model for Surface Electromyographic Signal Denoising

https://github.com/toosi/feedback_feedforward_alignment

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Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward Alignment

https://github.com/topwasu/ddvi

공식 코드

Denoising Diffusion Variational Inference: Diffusion Models as Expressive Variational Posteriors

https://github.com/torchddm/ddm

공식 코드

Diffusion Deformable Model for 4D Temporal Medical Image Generation

https://github.com/torrvision/straighttoshapes

공식 코드

Straight to Shapes: Real-time Detection of Encoded Shapes

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