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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/Saurav-K-Shastri/deepECpr

공식 코드

Fast and Robust Phase Retrieval via Deep Expectation-Consistent Approximation

https://github.com/SebastianJanampa/DT-LSD

공식 코드

DT-LSD: Deformable Transformer-based Line Segment Detection

https://github.com/SehwanChoi0307/Mask2Map

공식 코드

Mask2Map: Vectorized HD Map Construction Using Bird's Eye View Segmentation Masks

https://github.com/Seolen/weak_seg_via_shape_model

공식 코드

Weakly Supervised Volumetric Segmentation via Self-taught Shape Denoising Model

https://github.com/Shakarim94/Net-SURE

공식 코드

Training Deep Learning Based Denoisers without Ground Truth Data

https://github.com/SherifAbdulatif/CMGAN

공식 코드

CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement

https://github.com/ShinyWang33/eSL-Net

공식 코드

Event Enhanced High-Quality Image Recovery

https://github.com/ShirleyMaxx/VirtualMarker

공식 코드

3D Human Mesh Estimation from Virtual Markers

https://github.com/ShootingWong/DomainRAG

공식 코드

DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented Generation

https://github.com/Shorya-Sharma/Game-Theory-For-Adversarial-Attacks-And-Defenses

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Game Theory for Adversarial Attacks and Defenses

https://github.com/ShreyasFadnavis/patch2self

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Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning

https://github.com/Sindhu-Hegde/pseudo-visual-speech-denoising

공식 코드

Visual Speech Enhancement Without A Real Visual Stream

https://github.com/Smith42/astroddpm

공식 코드

Realistic galaxy image simulation via score-based generative models

https://github.com/StanislasDucotterd/Smooth-Plus-Sparse-Model

공식 코드

Learning of Patch-Based Smooth-Plus-Sparse Models for Image Reconstruction

https://github.com/SteveKommrusch/VRepair

공식 코드

Neural Transfer Learning for Repairing Security Vulnerabilities in C Code

https://github.com/Stream-AD/AnoGraph

공식 코드

Streaming Anomaly Detection

https://github.com/Stream-AD/MIDAS

공식 코드

Streaming Anomaly Detection

https://github.com/Stream-AD/MemStream

공식 코드

MemStream: Memory-Based Streaming Anomaly Detection

https://github.com/Streaming-Diffusion-Policy/streaming_diffusion_policy

공식 코드

Streaming Diffusion Policy: Fast Policy Synthesis with Variable Noise Diffusion Models

https://github.com/SunayBhat1/PureGen_PoisonDefense

공식 코드

PureGen: Universal Data Purification for Train-Time Poison Defense via Generative Model Dynamics

https://github.com/TalRemez/deep_class_aware_denoising

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Deep Class Aware Denoising

https://github.com/Talented-Q/Restorer

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Restorer: Removing Multi-Degradation with All-Axis Attention and Prompt Guidance

https://github.com/TaoHuang2018/Neighbor2Neighbor

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Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images

https://github.com/Tencent/BlockFusion

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BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation

https://github.com/Thinklab-SJTU/R3Det_Tensorflow

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SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing

https://github.com/Thinklab-SJTU/S2TLD

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SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing

https://github.com/TimRoith/BregmanLearning

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A Bregman Learning Framework for Sparse Neural Networks

https://github.com/Time-Travel-Rephotography/Time-Travel-Rephotography.github.io

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Time-Travel Rephotography

https://github.com/TingweiMeng/HJ_nonadditive_denoise

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On Hamilton-Jacobi PDEs and image denoising models with certain non-additive noise

https://github.com/TitorX/GVCFDiffusion

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Guided and Variance-Corrected Fusion with One-shot Style Alignment for Large-Content Image Generation

https://github.com/Tom-Achache/QAEs

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Denoising quantum states with Quantum Autoencoders -- Theory and Applications

https://github.com/ToniCreswell/DAAE_

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Denoising Adversarial Autoencoders

https://github.com/ToyotaResearchInstitute/att-aware

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MAAD: A Model and Dataset for "Attended Awareness" in Driving

https://github.com/UK-Digital-Heart-Project/4Dsurvival

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Deep learning cardiac motion analysis for human survival prediction

https://github.com/UT-Mao/Initial-Noise-Construction

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The Lottery Ticket Hypothesis in Denoising: Towards Semantic-Driven Initialization

https://github.com/UT-Mao/Initial-Noise-Editing

공식 코드

Guided Image Synthesis via Initial Image Editing in Diffusion Model

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