https://github.com/gudaochangsheng/MaskUnet
공식 코드Not All Parameters Matter: Masking Diffusion Models for Enhancing Generation Ability
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/guhuangai/ddm-public
공식 코드Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image Attenuation
https://github.com/guhuangai/diffusionedge
공식 코드DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection
https://github.com/guillaumejs2403/ace
공식 코드https://github.com/gulucaptain/dynamictrl
공식 코드https://github.com/gulucaptain/videoassembler
공식 코드MagDiff: Multi-Alignment Diffusion for High-Fidelity Video Generation and Editing
https://github.com/guochengqian/TENet
공식 코드Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline
https://github.com/guoqincode/focus-on-your-instruction
공식 코드Focus on Your Instruction: Fine-grained and Multi-instruction Image Editing by Attention Modulation
https://github.com/guoqiushan/egc
공식 코드EGC: Image Generation and Classification via a Diffusion Energy-Based Model
https://github.com/guoshi28/2stagealign
공식 코드A Differentiable Two-stage Alignment Scheme for Burst Image Reconstruction with Large Shift
https://github.com/guoshi28/vjdd
공식 코드Toward Accurate and Temporally Consistent Video Restoration from Raw Data
https://github.com/guoyaoshen/rnst-regularization_by_nst
공식 코드Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited Data
https://github.com/guyuxuan9/tensorllm
공식 코드TensorLLM: Tensorising Multi-Head Attention for Enhanced Reasoning and Compression in LLMs
https://github.com/gyukai/i2vc
공식 코드I$^2$VC: A Unified Framework for Intra- & Inter-frame Video Compression
https://github.com/h2o64/slips
공식 코드https://github.com/haha-lisa/Diffstyler
공식 코드DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization
https://github.com/haha-lisa/style-a-video
공식 코드Style-A-Video: Agile Diffusion for Arbitrary Text-based Video Style Transfer
https://github.com/hai-chao-zhang/oostraj
공식 코드OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising
https://github.com/haiduo/deepkd
공식 코드DeepKD: A Deeply Decoupled and Denoised Knowledge Distillation Trainer
https://github.com/haihabi/generativecrb
공식 코드https://github.com/hamedhaghighi/lidargrit
공식 코드Taming Transformers for Realistic Lidar Point Cloud Generation
https://github.com/hamidreza-dastmalchi/3dd-tta
공식 코드Test-Time Adaptation of 3D Point Clouds via Denoising Diffusion Models
https://github.com/hanbyel0105/diff-hmr
공식 코드Generative Approach for Probabilistic Human Mesh Recovery using Diffusion Models
https://github.com/handsomewzy/diffv2iqa
공식 코드https://github.com/hanshounsu/d3rm
공식 코드D3RM: A Discrete Denoising Diffusion Refinement Model for Piano Transcription
https://github.com/hanshuyan/adjointdpm
공식 코드https://github.com/hao1635/ascon
공식 코드ASCON: Anatomy-aware Supervised Contrastive Learning Framework for Low-dose CT Denoising
https://github.com/hao1635/langmamba
공식 코드LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models
https://github.com/hao1635/leda
공식 코드Low-dose CT Denoising with Language-engaged Dual-space Alignment
https://github.com/hao1635/lit-former
공식 코드https://github.com/haochen-rye/hnerv
공식 코드https://github.com/haochen-rye/nerv
공식 코드https://github.com/haochen27/ZS-FLIM-Denoising
공식 코드Zero-Shot Denoising for Fluorescence Lifetime Imaging Microscopy with Intensity-Guided Learning
https://github.com/haofengl/dragnoise
공식 코드Drag Your Noise: Interactive Point-based Editing via Diffusion Semantic Propagation
https://github.com/haoningwu3639/MegaFusion
공식 코드https://github.com/haoqinhong/ccspnet-joint
공식 코드CCSPNet-Joint: Efficient Joint Training Method for Traffic Sign Detection Under Extreme Conditions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.