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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/fqnchina/DecoupleLearning

공식 코드

Decouple Learning for Parameterized Image Operators

https://github.com/francescodifeola/iqa-denosing

공식 코드

A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT Denoising

https://github.com/francis-hsu/quantgenmdl

공식 코드

Generative quantum machine learning via denoising diffusion probabilistic models

https://github.com/frank-zy-dou/emdm

공식 코드

EMDM: Efficient Motion Diffusion Model for Fast and High-Quality Motion Generation

https://github.com/franziska-schirrmacher/AQuaSI

공식 코드

Adaptive Quantile Sparse Image (AQuaSI) Prior for Inverse Imaging Problems

https://github.com/frccvpr/frc-loss

공식 코드

Image quality measurements and denoising using Fourier Ring Correlations

https://github.com/freemasti/diffo

공식 코드

DiffO: Single-step Diffusion for Image Compression at Ultra-Low Bitrates

https://github.com/frostinassiky/denoiseloc

공식 코드

Boundary-Denoising for Video Activity Localization

https://github.com/frozenburning/primdiffusion

공식 코드

PrimDiffusion: Volumetric Primitives Diffusion for 3D Human Generation

https://github.com/fsherry/non-expansive-odes

공식 코드

Designing Stable Neural Networks using Convex Analysis and ODEs

https://github.com/fudan-zvg/diffusion-square

공식 코드

Diffusion$^2$: Dynamic 3D Content Generation via Score Composition of Video and Multi-view Diffusion Models

https://github.com/fuguoji/GSDN

공식 코드

Understanding Graph Neural Networks from Graph Signal Denoising Perspectives

https://github.com/furiocolombo/mamba-foley

공식 코드

MambaFoley: Foley Sound Generation using Selective State-Space Models

https://github.com/futurexiang/ddae

공식 코드

Denoising Diffusion Autoencoders are Unified Self-supervised Learners

https://github.com/fvl2020/mb-taylorformerv2

공식 코드

MB-TaylorFormer V2: Improved Multi-branch Linear Transformer Expanded by Taylor Formula for Image Restoration

https://github.com/g-u-n/animatelcm

공식 코드

AnimateLCM: Computation-Efficient Personalized Style Video Generation without Personalized Video Data

https://github.com/g-u-n/gen-l-video

공식 코드

Gen-L-Video: Multi-Text to Long Video Generation via Temporal Co-Denoising

https://github.com/gal4way/tpd

공식 코드

Texture-Preserving Diffusion Models for High-Fidelity Virtual Try-On

https://github.com/gang-qu/hatnet-spi

공식 코드

Dual-Scale Transformer for Large-Scale Single-Pixel Imaging

https://github.com/ganggit/jointmodel

공식 코드

Joint Visual Denoising and Classification using Deep Learning

https://github.com/gaparmar/img2img-turbo

공식 코드

One-Step Image Translation with Text-to-Image Models

https://github.com/garvita-tiwari/PoseNDF

공식 코드

Pose-NDF: Modeling Human Pose Manifolds with Neural Distance Fields

https://github.com/gaurav71531/DeAn

공식 코드

Noisy Batch Active Learning with Deterministic Annealing

https://github.com/gbpcosta/wvc_2016_noise

공식 코드

An empirical study on the effects of different types of noise in image classification tasks

https://github.com/gbuzzard/generative-pnp-allerton

공식 코드

Generative Plug and Play: Posterior Sampling for Inverse Problems

https://github.com/gcunhase/StackedDeBERT

공식 코드

Stacked DeBERT: All Attention in Incomplete Data for Text Classification

https://github.com/ge-xing/diff-unet

공식 코드

Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation

https://github.com/geekguru123/profilingdit

공식 코드

Model Reveals What to Cache: Profiling-Based Feature Reuse for Video Diffusion Models

https://github.com/gemini-breeding/sogm_soil_spectra_simulation

공식 코드

A text-based, generative deep learning model for soil reflectance spectrum simulation in the VIS-NIR (400-2499 nm) bands

https://github.com/gen-verse/diffusion-sharpening

공식 코드

Diffusion-Sharpening: Fine-tuning Diffusion Models with Denoising Trajectory Sharpening

https://github.com/genentech/voxbind

공식 코드

Structure-based drug design by denoising voxel grids

https://github.com/genentech/voxmol

공식 코드

3D molecule generation by denoising voxel grids

https://github.com/genforce/mganprior

공식 코드

Image Processing Using Multi-Code GAN Prior

https://github.com/geokarant/incsar

공식 코드

IncSAR: A Dual Fusion Incremental Learning Framework for SAR Target Recognition

https://github.com/gfrancisco20/video_diffusion

공식 코드

Generative Simulations of The Solar Corona Evolution With Denoising Diffusion : Proof of Concept

https://github.com/gfxdisp/colorvideovdp

공식 코드

Training Neural Networks on RAW and HDR Images for Restoration Tasks

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