Papers with Code 아카이브
Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/mahmad00/conventional-to-transformer-for-hyperspectral-image-classification-survey-2024
공식 코드https://github.com/maikwischow/noise-source-estimation
공식 코드Real-time Noise Source Estimation of a Camera System from an Image and Metadata
https://github.com/majedelhelou/BIGPrior
공식 코드BIGPrior: Towards Decoupling Learned Prior Hallucination and Data Fidelity in Image Restoration
https://github.com/majedelhelou/BUIFD
공식 코드Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning
https://github.com/majedelhelou/SFM
공식 코드Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks
https://github.com/majibear000/roppt
공식 코드https://github.com/majitao-xd/bsdm-had
공식 코드BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly Detection
https://github.com/malshav/sar_ddpm
공식 코드SAR Despeckling using a Denoising Diffusion Probabilistic Model
https://github.com/malshav/sar_transformer
공식 코드https://github.com/manem-lab/lung-ddpm
공식 코드Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image Synthesis
https://github.com/manuelmlmadeira/DeFoG
공식 코드https://github.com/maowenyu-11/TDM
공식 코드Addressing Missing Data Issue for Diffusion-based Recommendation
https://github.com/marc-rigter/polygrad-world-models
공식 코드https://github.com/marcorossi5/DUNEdn
공식 코드https://github.com/mariasi1/deeporientationnetmodel
공식 코드DeepOrientation: convolutional neural network for fringe pattern orientation map estimation
https://github.com/marien-renaud/snore
공식 코드Plug-and-Play image restoration with Stochastic deNOising REgularization
https://github.com/mariiadrozdova/diffusion-for-sources-characterisation
공식 코드Radio-astronomical Image Reconstruction with Conditional Denoising Diffusion Model
https://github.com/mariomorvan/denoising-time-series-transformer
공식 코드https://github.com/mariusarvinte/wavelet-patch-detection
공식 코드https://github.com/marksgraham/ddpm-ood
공식 코드Denoising diffusion models for out-of-distribution detection
https://github.com/markxcloud/cspd
공식 코드Continuous Speculative Decoding for Autoregressive Image Generation
https://github.com/masa-ue/prodifevo-refinement
공식 코드https://github.com/mashathepotato/GSoC-Quantum-Diffusion-Model
공식 코드https://github.com/mateuszpoleski/geoguide
공식 코드https://github.com/mathpluscode/imgx-diffseg
공식 코드https://github.com/matt-bendel/pcagan
공식 코드pcaGAN: Improving Posterior-Sampling cGANs via Principal Component Regularization
https://github.com/matthieutrs/fire
공식 코드FiRe: Fixed-points of Restoration Priors for Solving Inverse Problems
https://github.com/mattkingros/MEM-Denoising-and-Deblurring
공식 코드Data-Driven Priors in the Maximum Entropy on the Mean Method for Linear Inverse Problems
https://github.com/maybelizzy/diffusion_ood_robustness
공식 코드How Does Diffusion Influence Pretrained Language Models on Out-of-Distribution Data?
https://github.com/mayo-radiology-informatics-lab/mbti
공식 코드Multitask Brain Tumor Inpainting with Diffusion Models: A Methodological Report
https://github.com/mbahri/KDRSDL
공식 코드Robust Kronecker-Decomposable Component Analysis for Low-Rank Modeling
https://github.com/mbkiss/2detectcodes
공식 코드https://github.com/mbrossar/denoise-imu-gyro
공식 코드Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation
https://github.com/mcg-nju/jomold
공식 코드Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsing
https://github.com/mcgill-nlp/diffusion-itm
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.