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/chxy95/hat
https://github.com/cig-skoltech/burst-cvpr-2019
https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations
https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations/tree/main/Diffusion/DDIM
https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations/tree/main/Diffusion/DDPM
https://github.com/clarken92/vfm
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/clausmichele/ViDeNN
https://github.com/clberube/ip-vae
https://github.com/climsocana/tecb-de
https://github.com/clintonjwang/controlnet
https://github.com/clmbrs/communication-translation
Multilingual Denoising Pre-training for Neural Machine Translation
https://github.com/clu0/unet.cu
https://github.com/code-implementation1/Code1/tree/main/DnCNN
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/code-implementation1/Code3/tree/main/FaceQualityAssessment
https://github.com/code-implementation1/Code4/tree/main/IndexNet
https://github.com/code-implementation1/Code4/tree/main/LearningToSeeInTheDark
https://github.com/code-implementation1/Code5/tree/main/llnet
LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
https://github.com/code-implementation1/Code5/tree/main/mifnet
GCN-MIF: Graph Convolutional Network with Multi-Information Fusion for Low-dose CT Denoising
https://github.com/code-implementation1/Code6/tree/main/Neighbor2Neighbor
Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images
https://github.com/code-implementation1/Code7/tree/main/RDN
https://github.com/codearxiv/continuous-K-SVD
Cloud Dictionary: Sparse Coding and Modeling for Point Clouds
https://github.com/codelion/adaptive-classifier
AquaSignal: An Integrated Framework for Robust Underwater Acoustic Analysis
https://github.com/coloquinte/torchsr
https://github.com/compscikaran/nightlight-web
https://github.com/compvis/fm-boosting
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/compvis/imagebart
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
https://github.com/compvis/latent-diffusion
https://github.com/compvis/stable-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/cosmoquester/2021-dialogue-summary-competition
https://github.com/cosmoquester/transformers-bart-pretrain
https://github.com/crowsonkb/v-diffusion-jax
https://github.com/csjunxu/PolyU-Real-World-Noisy-Images-Dataset
https://github.com/csm9493/FC-AIDE
https://github.com/cswry/osediff
Adversarial Diffusion Compression for Real-World Image Super-Resolution
https://github.com/cszhilu1998/selfhdr
Exposure Bracketing Is All You Need For A High-Quality Image
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.