Papers with Code 아카이브
Denoising
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 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/bioinfomachinelearning/bio-diffusion
공식 코드Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
https://github.com/bioinfomachinelearning/gcdm-sbdd
공식 코드Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
https://github.com/biomathlab/PDE-Learning-few-time-samples
공식 코드Learning Equations from Biological Data with Limited Time Samples
https://github.com/biomedai-ucsc/inversesr
공식 코드InverseSR: 3D Brain MRI Super-Resolution Using a Latent Diffusion Model
https://github.com/bird-tao/gntpp
공식 코드https://github.com/bit-da/dusa
공식 코드Exploring Structured Semantic Priors Underlying Diffusion Score for Test-time Adaptation
https://github.com/bit-ml/dolos
공식 코드Weakly-supervised deepfake localization in diffusion-generated images
https://github.com/bit-vision/rsir
공식 코드Recurrent Spike-based Image Restoration under General Illumination
https://github.com/bjing2016/subspace-diffusion
공식 코드https://github.com/bjmch/dl-radiotransient
공식 코드Deep learning-based deconvolution for interferometric radio transient reconstruction
https://github.com/bmmi/denoising-fluorescence
공식 코드A Poisson-Gaussian Denoising Dataset with Real Fluorescence Microscopy Images
https://github.com/bonaldli/visiontraj
공식 코드VisionTraj: A Noise-Robust Trajectory Recovery Framework based on Large-scale Camera Network
https://github.com/bosecorp/devo
공식 코드Self-Supervised Learning for Speech Enhancement through Synthesis
https://github.com/bregaldo/stat_comp_sep
공식 코드Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures
https://github.com/brian-moser/diwa
공식 코드Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolution
https://github.com/bronemos/view-fusion
공식 코드ViewFusion: Learning Composable Diffusion Models for Novel View Synthesis
https://github.com/bruno-31/groupsc
공식 코드Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration
https://github.com/bruno686/dcf
공식 코드https://github.com/bsmhmmlf/Gaussian-Shading
공식 코드Gaussian Shading: Provable Performance-Lossless Image Watermarking for Diffusion Models
https://github.com/btolooshams/stable-interpretable-unrolled-dl
공식 코드https://github.com/bu-cisl/DynamicSyntesisNetwork
공식 코드https://github.com/buggyyang/rvd
공식 코드https://github.com/bujiazi/motionclone
공식 코드MotionClone: Training-Free Motion Cloning for Controllable Video Generation
https://github.com/bupt-ai-cz/SMAF
공식 코드https://github.com/byeonghu-na/tdsm
공식 코드https://github.com/byeongjun-park/DTR
공식 코드https://github.com/byeongjun-park/Switch-DiT
공식 코드Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts
https://github.com/bytedance/1d-tokenizer
공식 코드https://github.com/bzho3923/gnn_dot
공식 코드https://github.com/c-yn/adair
공식 코드AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation
https://github.com/c0natus/D3Rec
공식 코드https://github.com/cadurosar/benchmark_graphinference
공식 코드https://github.com/caizeyu1992/sst
공식 코드https://github.com/callsys/genpromp
공식 코드Generative Prompt Model for Weakly Supervised Object Localization
https://github.com/calvinyang0/crnet
공식 코드CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.