https://github.com/kritiksoman/GIMP-ML
공식 코드GIMP-ML: Python Plugins for using Computer Vision Models in GIMP
Papers with Code 아카이브
Blind Image Deblurring
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Blind Image Deblurring is a classical problem in image processing and computer vision, which aims to recover a latent image from a blurred input.
Source: Learning a Discriminative Prior for Blind Image Deblurring
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.