Papers with Code 아카이브

Image Deblurring

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 9
보존된 평가 기록: 73

file:///var/mobile/Library/SMS/Attachments/cb/11/8A26E2FC-7464-4692-8F2A-3F1B3E23BCD5/IMG_4630.jpeg

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/theophil-trippe/HDC_TUBerlin_version_1

공식 코드

Let's Enhance: A Deep Learning Approach to Extreme Deblurring of Text Images

https://github.com/tirer-lab/ddpg

공식 코드

Image Restoration by Denoising Diffusion Models with Iteratively Preconditioned Guidance

https://github.com/tombs98/mhnet

공식 코드

Mixed Hierarchy Network for Image Restoration

https://github.com/tombs98/sfafnet

공식 코드

Spatial and Frequency Domain Adaptive Fusion Network for Image Deblurring

https://github.com/tomtirer/IDBP

공식 코드

Image Restoration by Iterative Denoising and Backward Projections

https://github.com/ustclby/Unsupervised-Domain-Specific-Deblurring

공식 코드

Unsupervised Domain-Specific Deblurring via Disentangled Representations

https://github.com/vpronina/DeepWienerRestoration

공식 코드

Microscopy Image Restoration with Deep Wiener-Kolmogorov filters

https://github.com/wenlongjiao/frenet

공식 코드

Efficient RAW Image Deblurring with Adaptive Frequency Modulation

https://github.com/wlydlut/c2f-dft

공식 코드

Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration

https://github.com/wujunde/medsegdiff

공식 코드

MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model

https://github.com/wyhuai/ddnm

공식 코드

Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model

https://github.com/xyliu339/osdd

공식 코드

One-Step Diffusion Model for Image Motion-Deblurring

https://github.com/yeeesir/dvs_rdnet

공식 코드

Restoring Real-World Degraded Events Improves Deblurring Quality

https://github.com/yjzhang96/motion-etr

공식 코드

Exposure Trajectory Recovery from Motion Blur

https://github.com/yjzhang96/neurmap-deblur

공식 코드

Neural Maximum A Posteriori Estimation on Unpaired Data for Motion Deblurring

https://github.com/yuanzhi-zhu/diffpir

공식 코드

Denoising Diffusion Models for Plug-and-Play Image Restoration

https://github.com/yulunzhang/RDN

공식 코드

Residual Dense Network for Image Restoration

https://github.com/z-fabian/flash-diffusion

공식 코드

Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models

https://github.com/zeyuxiao1997/EDPN

공식 코드

EDPN: Enhanced Deep Pyramid Network for Blurry Image Restoration

https://github.com/zhengchen1999/hi-diff

공식 코드

Hierarchical Integration Diffusion Model for Realistic Image Deblurring

https://github.com/zhihongz/infwide

공식 코드

INFWIDE: Image and Feature Space Wiener Deconvolution Network for Non-blind Image Deblurring in Low-Light Conditions

https://github.com/zihuiwu/PnP-DM-public

공식 코드

Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors

https://github.com/zpc456/akmd-net

공식 코드

Jointly RS Image Deblurring and Super-Resolution with Adjustable-Kernel and Multi-Domain Attention

https://github.com/zsc15/HPPP

공식 코드

HPPP: Halpern-type Preconditioned Proximal Point Algorithms and Applications to Image Restoration

https://github.com/zzh-tech/ESTRNN

공식 코드

Real-world Video Deblurring: A Benchmark Dataset and An Efficient Recurrent Neural Network

https://github.com/zzr-idam/deblur

공식 코드

Ultra-High-Definition Image Deblurring via Multi-scale Cubic-Mixer

https://github.com/zzr-idam/mixnet

공식 코드

MixNet: Efficient Global Modeling for Ultra-High-Definition Image Restoration

https://gitlab.mpi-klsb.mpg.de/jdong/dwdn

공식 코드

DWDN: Deep Wiener Deconvolution Network for Non-Blind Image Deblurring

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.