https://github.com/calvinyang0/crnet
공식 코드CRNet: A Detail-Preserving Network for Unified Image Restoration and Enhancement Task
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/caojiezhang/davsr
공식 코드Towards Interpretable Video Super-Resolution via Alternating Optimization
https://github.com/cgtuebingen/learning-blind-motion-deblurring
공식 코드https://github.com/chaowentao/maskblur
공식 코드MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-Resolution
https://github.com/chen-rao/vd-diff
공식 코드Rethinking Video Deblurring with Wavelet-Aware Dynamic Transformer and Diffusion Model
https://github.com/chengxuliu/miscfilter
공식 코드Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring
https://github.com/chenkang455/s-sdm
공식 코드SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams
https://github.com/chensong1995/deblursr
공식 코드DeblurSR: Event-Based Motion Deblurring Under the Spiking Representation
https://github.com/chensong1995/e-cir
공식 코드https://github.com/chensong1995/ppln
공식 코드PPLNs: Parametric Piecewise Linear Networks for Event-Based Temporal Modeling and Beyond
https://github.com/chosj95/mimo-unet
공식 코드Rethinking Coarse-to-Fine Approach in Single Image Deblurring
https://github.com/chrischen1023/sem-net
공식 코드SEM-Net: Efficient Pixel Modelling for image inpainting with Spatially Enhanced SSM
https://github.com/cidautai/darkir
공식 코드https://github.com/ckkelvinchan/basicvsr_plusplus
공식 코드On the Generalization of BasicVSR++ to Video Deblurring and Denoising
https://github.com/claroche-r/fastdiffusionem
공식 코드Fast Diffusion EM: a diffusion model for blind inverse problems with application to deconvolution
https://github.com/claroche-r/pnp_ladmm
공식 코드Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying Kernels
https://github.com/cmlab-korea/awesome-3d-low-level-vision
공식 코드R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision
https://github.com/codeslake/IFAN
공식 코드Iterative Filter Adaptive Network for Single Image Defocus Deblurring
https://github.com/codeslake/PVDNet
공식 코드Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes
https://github.com/codeslake/RealTime_VDBLR
공식 코드Real-Time Video Deblurring via Lightweight Motion Compensation
https://github.com/computational-imaging/lsvpe
공식 코드Learning Spatially Varying Pixel Exposures for Motion Deblurring
https://github.com/contgenmod/continuous-generative-neural-network
공식 코드Continuous Generative Neural Networks: A Wavelet-Based Architecture in Function Spaces
https://github.com/csbhr/CDVD-TSP
공식 코드Cascaded Deep Video Deblurring Using Temporal Sharpness Prior
https://github.com/csdwren/SelfDeblur
공식 코드https://github.com/cszcwu/gkmnet
공식 코드Gaussian Kernel Mixture Network for Single Image Defocus Deblurring
https://github.com/cszhilu1998/bracketire
공식 코드Exposure Bracketing Is All You Need For A High-Quality Image
https://github.com/cszhilu1998/selfir
공식 코드Self-Supervised Image Restoration with Blurry and Noisy Pairs
https://github.com/cszn/DPIR
공식 코드https://github.com/cszn/DPSR
공식 코드Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels
https://github.com/cszn/ircnn
공식 코드https://github.com/dachunkai/ev-deblurvsr
공식 코드https://github.com/daidaijr/eamamba
공식 코드EAMamba: Efficient All-Around Vision State Space Model for Image Restoration
https://github.com/dali92002/DE-GAN
공식 코드DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement
https://github.com/dasongli1/learning_degradation
공식 코드https://github.com/dasongli1/shift-net
공식 코드A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift
https://github.com/dch0319/glkm-deblur
공식 코드Generative Latent Kernel Modeling for Blind Motion Deblurring
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.