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/jingyzhang/cgr
공식 코드https://github.com/jinjianRick/DCI_ICO
공식 코드Customized Generation Reimagined: Fidelity and Editability Harmonized
https://github.com/jinlin2021/fap_cd
공식 코드FAP-CD: Fairness-Driven Age-Friendly Community Planning via Conditional Diffusion Generation
https://github.com/jinnh/resss-convset
공식 코드Deep Diversity-Enhanced Feature Representation of Hyperspectral Images
https://github.com/jinyiw/guideddiffusionpur
공식 코드https://github.com/jiwoogit/arbooth
공식 코드Fine-Tuning Visual Autoregressive Models for Subject-Driven Generation
https://github.com/jiyuanfeng/ddp
공식 코드https://github.com/jjhuangcs/WINNet
공식 코드WINNet: Wavelet-inspired Invertible Network for Image Denoising
https://github.com/jlinki/JDR
공식 코드Joint Graph Rewiring and Feature Denoising via Spectral Resonance
https://github.com/jm-xiong/rshazediff
공식 코드RSHazeDiff: A Unified Fourier-aware Diffusion Model for Remote Sensing Image Dehazing
https://github.com/jmiramont/mcsm-benchs
공식 코드Benchmarking multi-component signal processing methods in the time-frequency plane
https://github.com/jmiramont/spectrogram-zeros-classification
공식 코드https://github.com/jmyoon1/adp
공식 코드https://github.com/joaquinr-uner/tvwse
공식 코드Fully Adaptive Time-Varying Wave-Shape Model: Applications in Biomedical Signal Processing
https://github.com/joeyy5588/planning-as-inpainting
공식 코드https://github.com/joezhao527/dm4neuro
공식 코드https://github.com/johanna-rock/imRICnn
공식 코드https://github.com/johannbrehmer/manifold-flow
공식 코드Flows for simultaneous manifold learning and density estimation
https://github.com/johertrich/Proximal_Neural_Networks
공식 코드Convolutional Proximal Neural Networks and Plug-and-Play Algorithms
https://github.com/john-vastola/gtv-iclr25
공식 코드Generalization through variance: how noise shapes inductive biases in diffusion models
https://github.com/john-venti/self-dace
공식 코드Self-Reference Deep Adaptive Curve Estimation for Low-Light Image Enhancement
https://github.com/join16/leap
공식 코드Learning to Enhance Aperture Phasor Field for Non-Line-of-Sight Imaging
https://github.com/jojo23333/STPAN
공식 코드https://github.com/jonasricker/aeroblade
공식 코드AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error
https://github.com/jonastorz/physical_spline
공식 코드https://github.com/jonathancrabbe/fourierdiffusion
공식 코드https://github.com/jonathanventura/self-supervised-poisson-gaussian
공식 코드https://github.com/jongwon20000/TRACA
공식 코드Context-aware Deep Feature Compression for High-speed Visual Tracking
https://github.com/joppli/xrd_denoising
공식 코드Weak-signal extraction enabled by deep-neural-network denoising of diffraction data
https://github.com/josephcao0327/stylefool
공식 코드StyleFool: Fooling Video Classification Systems via Style Transfer
https://github.com/joshyZhou/HINT
공식 코드Devil is in the Uniformity: Exploring Diverse Learners within Transformer for Image Restoration
https://github.com/jp-guo/codiff
공식 코드Compression-Aware One-Step Diffusion Model for JPEG Artifact Removal
https://github.com/jp-guo/oscar
공식 코드OSCAR: One-Step Diffusion Codec for Image Compression Across Multiple Bit-rates
https://github.com/jpzhang1810/ldm-robustness
공식 코드https://github.com/jquetzalcoatl/LSD-GANs
공식 코드Using Deep LSD to build operators in GANs latent space with meaning in real space
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.