Papers with Code 아카이브
Blind Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 18
- 보존된 평가 기록
- 52
Blind Super-Resolution is an image processing technique that aims to reconstruct high-resolution images from low-resolution counterparts without prior knowledge of the degradation process.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Taming Stable Diffusion for Computed Tomography Blind Super-Resolution
Adaptive Blind Super-Resolution Network for Spatial-Specific and Spatial-Agnostic Degradations
EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution
KernelFusion: Assumption-Free Blind Super-Resolution via Patch Diffusion
Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super Resolution
RFSR: Improving ISR Diffusion Models via Reward Feedback Learning
Blind Time-of-Flight Imaging: Sparse Deconvolution on the Continuum with Unknown Kernels
Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution
Image Super-Resolution with Taylor Expansion Approximation and Large Field Reception
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation
A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution
AddSR: Accelerating Diffusion-based Blind Super-Resolution with Adversarial Diffusion Distillation
Deep Blind Super-Resolution for Satellite Video
Blind Image Super-resolution with Rich Texture-Aware Codebooks
Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-Resolution
End-to-end Alternating Optimization for Real-World Blind Super Resolution
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution
StarSRGAN: Improving Real-World Blind Super-Resolution
Scale Guided Hypernetwork for Blind Super-Resolution Image Quality Assessment
Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIs
Exploiting Diffusion Prior for Real-World Image Super-Resolution
Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images
Kernelized Back-Projection Networks for Blind Super Resolution
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution
Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation
Learning Detail-Structure Alternative Optimization for Blind Super-Resolution
Knowledge Distillation based Degradation Estimation for Blind Super-Resolution
The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks
Blind Super-Resolution for Remote Sensing Images via Conditional Stochastic Normalizing Flows
KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution
Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution
Meta-Learning based Degradation Representation for Blind Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.