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Super-Resolution

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하위 태스크: 6
평가표: 1
보존된 평가 기록: 1

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

<span style="color:grey; opacity: 0.6">( Credit: MemNet )</span>

코드

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

https://github.com/cswin/RC-Nets

공식 코드

Image Restoration Using Deep Regulated Convolutional Networks

https://github.com/cswry/osediff

공식 코드

One-Step Effective Diffusion Network for Real-World Image Super-Resolution

https://github.com/cswry/seesr

공식 코드

SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution

https://github.com/csxmli2016/DFDNet

공식 코드

Blind Face Restoration via Deep Multi-scale Component Dictionaries

https://github.com/csxmli2016/GFRNet

공식 코드

Learning Warped Guidance for Blind Face Restoration

https://github.com/csxmli2016/marconet

공식 코드

Learning Generative Structure Prior for Blind Text Image Super-resolution

https://github.com/csxmli2016/redegnet

공식 코드

From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution

https://github.com/cszhilu1998/bracketire

공식 코드

Exposure Bracketing Is All You Need For A High-Quality Image

https://github.com/cszhilu1998/selfdzsr

공식 코드

Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations

https://github.com/cszhilu1998/selfdzsr_plusplus

공식 코드

Self-Supervised Learning for Real-World Super-Resolution from Dual and Multiple Zoomed Observations

https://github.com/cszn/BSRGAN

공식 코드

Designing a Practical Degradation Model for Deep Blind Image Super-Resolution

https://github.com/cszn/DPIR

공식 코드

Plug-and-Play Image Restoration with Deep Denoiser Prior

https://github.com/cszn/DPSR

공식 코드

Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels

https://github.com/cszn/DnCNN

공식 코드

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

https://github.com/cszn/KAIR

공식 코드

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

https://github.com/cszn/SRMD

공식 코드

Learning a Single Convolutional Super-Resolution Network for Multiple Degradations

https://github.com/cszn/USRNet

공식 코드

Deep Unfolding Network for Image Super-Resolution

https://github.com/cszn/ircnn

공식 코드

Learning Deep CNN Denoiser Prior for Image Restoration

https://github.com/cuhk-aim-group/star-rl

공식 코드

Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution

https://github.com/cuiziteng/eccv_aeris

공식 코드

Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection

https://github.com/cv-junchengli/sisr-survey

공식 코드

A Systematic Survey of Deep Learning-based Single-Image Super-Resolution

https://github.com/cvlab-stonybrook/ZoomLDM

공식 코드

ZoomLDM: Latent Diffusion Model for multi-scale image generation

https://github.com/cyiyoo/SurfaceReconstructionFromFewSlices

공식 코드

Super-Resolution Surface Reconstruction from Few Low-Resolution Slices

https://github.com/czlwang/brainbert

공식 코드

BrainBERT: Self-supervised representation learning for intracranial recordings

https://github.com/da-music/tsp22

공식 코드

DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm

https://github.com/da-music/tvt23

공식 코드

DA-MUSIC: Data-Driven DoA Estimation via Deep Augmented MUSIC Algorithm

https://github.com/dachunkai/ev-deblurvsr

공식 코드

Event-Enhanced Blurry Video Super-Resolution

https://github.com/dachunkai/evtexture

공식 코드

EvTexture: Event-driven Texture Enhancement for Video Super-Resolution

https://github.com/dahoas/edm

공식 코드

DFU: scale-robust diffusion model for zero-shot super-resolution image generation

https://github.com/daidaijr/eamamba

공식 코드

EAMamba: Efficient All-Around Vision State Space Model for Image Restoration

https://github.com/dallepezze/adcl_scale

공식 코드

Continual Learning Approaches for Anomaly Detection

https://github.com/dandingbudanding/BAM

공식 코드

BAM: A Balanced Attention Mechanism for Single Image Super Resolution

https://github.com/danfenghong/ECCV2020_CUCaNet

공식 코드

Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution

https://github.com/danielgetter/downscaling_srcnn

공식 코드

Statistical treatment of convolutional neural network super-resolution of inland surface wind for subgrid-scale variability quantification

https://github.com/danobohud/TransferLearningforPSC

공식 코드

Transfer Learning for Protein Structure Classification at Low Resolution

https://github.com/darwen9/ckmimagenet

공식 코드

Deep Learning-Based CKM Construction with Image Super-Resolution

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