https://github.com/kritiksoman/GIMP-ML
공식 코드GIMP-ML: Python Plugins for using Computer Vision Models in GIMP
Papers with Code 아카이브
Deblurring
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 16
- 보존된 평가 기록
- 199
Deblurring is a computer vision task that involves removing the blurring artifacts from images or videos to restore the original, sharp content. Blurring can be caused by various factors such as camera shake, fast motion, and out-of-focus objects, and can result in a loss of detail and quality in the captured images. The goal of deblurring is to produce a clear, high-quality image that accurately represents the original scene.
<span style="color:grey; opacity: 0.6">( Image credit: Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/laomao0/BIN
공식 코드https://github.com/lara-kassab/qtrk-code
공식 코드Quantile-Based Randomized Kaczmarz for Corrupted Tensor Linear Systems
https://github.com/lcav/lenslesspicam
공식 코드https://github.com/lehduong/onediffusion
공식 코드https://github.com/lenka98/Bind-Deblurring-using-GANs
공식 코드https://github.com/leonmakise/prores
공식 코드ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration
https://github.com/lifengshiwo/DIN
공식 코드Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration
https://github.com/lihenryhfl/diffusion_optimal_control
공식 코드https://github.com/lingyanruan/DRBNet
공식 코드Learning to Deblur using Light Field Generated and Real Defocus Images
https://github.com/lingyanruan/lakdnet
공식 코드https://github.com/linjing7/VR-Baseline
공식 코드https://github.com/lisiyaoATbnu/crcnet
공식 코드https://github.com/lisiyaoATbnu/low_rank_kernel
공식 코드https://github.com/liturout/psld
공식 코드Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models
https://github.com/liu-sd/multi-output-deblur
공식 코드https://github.com/lixinustc/awesome-diffusion-model-for-image-processing
공식 코드Diffusion Models for Image Restoration and Enhancement -- A Comprehensive Survey
https://github.com/liyucs/dpanet
공식 코드https://github.com/liyucs/jdrl
공식 코드Learning Single Image Defocus Deblurring with Misaligned Training Pairs
https://github.com/ljzycmd/rawblur
공식 코드Towards Real-World Video Deblurring by Exploring Blur Formation Process
https://github.com/ljzycmd/vdtr
공식 코드https://github.com/loibo/stabilize_neural_networks
공식 코드To be or not to be stable, that is the question: understanding neural networks for inverse problems
https://github.com/loibo/tobeornottobestable
공식 코드To be or not to be stable, that is the question: understanding neural networks for inverse problems
https://github.com/longzilicart/pip_universal
공식 코드https://github.com/lpj0/bde
공식 코드https://github.com/lsmlovefm/GAMD-Net
공식 코드https://github.com/luisvargasmieles/ls-mcmc
공식 코드https://github.com/luowyang/defusion
공식 코드Visual-Instructed Degradation Diffusion for All-in-One Image Restoration
https://github.com/mahdihosseini/1Shot-MaxPol
공식 코드https://github.com/mahmoudnafifi/CIE_XYZ_NET
공식 코드CIE XYZ Net: Unprocessing Images for Low-Level Computer Vision Tasks
https://github.com/mandt-lab/c-pigdm
공식 코드Fast Samplers for Inverse Problems in Iterative Refinement Models
https://github.com/mandt-lab/ci2rm
공식 코드Fast Samplers for Inverse Problems in Iterative Refinement Models
https://github.com/marien-renaud/pnp_ula_posterior_law_sensivity
공식 코드Plug-and-Play Posterior Sampling under Mismatched Measurement and Prior Models
https://github.com/marien-renaud/snore
공식 코드Plug-and-Play image restoration with Stochastic deNOising REgularization
https://github.com/matt-bendel/rcgan
공식 코드A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems
https://github.com/mccorbineau/iRestNet
공식 코드Deep Unfolding of a Proximal Interior Point Method for Image Restoration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.