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/g4vrel/DDPM
https://github.com/gabeguo/any-order-speculative-decoding
Discrete Diffusion Modeling by Estimating the Ratios of the Data Distribution
https://github.com/galaxygliese/Latent-DDBM
https://github.com/gaozhihan/PreDiff
PreDiff: Precipitation Nowcasting with Latent Diffusion Models
https://github.com/garibida/ReNoise-Inversion
https://github.com/geekyutao/inpaint-anything
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/georgeyiasemis/RIM-PyTorch
https://github.com/georgosgeorgos/few-shot-diffusion-models
https://github.com/ghif/mtae
Domain Generalization for Object Recognition with Multi-task Autoencoders
https://github.com/giannisdaras/sgilo
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
https://github.com/glouppe/info8010-deep-learning
https://github.com/gmalivenko/pynet-v2
Replacing Mobile Camera ISP with a Single Deep Learning Model
https://github.com/google-research/google-research/tree/master/unprocessing
https://github.com/google-research/pix2seq
https://github.com/google/RED
The Little Engine that Could: Regularization by Denoising (RED)
https://github.com/google/burst-denoising
https://github.com/google/lyra
https://github.com/goutamgmb/NTIRE21_BURSTSR
https://github.com/goutamgmb/deep-burst-sr
Deep Reparametrization of Multi-Frame Super-Resolution and Denoising
https://github.com/goutamgmb/deep-rep
Deep Reparametrization of Multi-Frame Super-Resolution and Denoising
https://github.com/guanhuaw/mirtorch
https://github.com/guijacquemet/CARE_Noise2VOID_googleColab
https://github.com/guillaumeboniface/super_resolution
https://github.com/gy65896/Enhancement-Access
https://github.com/gymoon10/Instance-Segmentation-with-SpatialEmbedding-CA
Restormer: Efficient Transformer for High-Resolution Image Restoration
https://github.com/halimiqi/www21
https://github.com/hankpipi/diffusers-hetu
https://github.com/hanyoseob/k-space-deep-learning
https://github.com/hanyoseob/pytorch-noise2void
https://github.com/haochen-wang409/ross
https://github.com/haoychen3/DMIH
Hiding Images in Diffusion Models by Editing Learned Score Functions
https://github.com/haozheliu-st/t-gate
https://github.com/happycaoyue/lld
https://github.com/harry24k/mida-pytorch
https://github.com/harshit0511/Unsupervised-Image-denoising
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.