Papers with Code 아카이브
Image Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 69
- 보존된 평가 기록
- 748
Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow
You Only Need One Step: Fast Super-Resolution with Stable Diffusion via Scale Distillation
Vision-Informed Flow Image Super-Resolution with Quaternion Spatial Modeling and Dynamic Flow Convolution
Bridging the Domain Gap: A Simple Domain Matching Method for Reference-based Image Super-Resolution in Remote Sensing
An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution
LKFormer: Large Kernel Transformer for Infrared Image Super-Resolution
Explaining the Implicit Neural Canvas: Connecting Pixels to Neurons by Tracing their Contributions
Efficient Image Super-Resolution via Symmetric Visual Attention Network
No-Clean-Reference Image Super-Resolution: Application to Electron Microscopy
Machine Learning-Based Malicious Vehicle Detection for Security Threats and Attacks in Vehicle Ad-hoc Network (VANET) Communications
Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary
Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach
Diffusion Models, Image Super-Resolution And Everything: A Survey
Beyond Subspace Isolation: Many-to-Many Transformer for Light Field Image Super-resolution
Compressing Deep Image Super-resolution Models
Image Super-resolution Reconstruction Network based on Enhanced Swin Transformer via Alternating Aggregation of Local-Global Features
Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-Resolution
Noise-free Optimization in Early Training Steps for Image Super-Resolution
Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer
Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction
BSRAW: Improving Blind RAW Image Super-Resolution
DDistill-SR: Reparameterized Dynamic Distillation Network for Lightweight Image Super-Resolution
EPNet: An Efficient Pyramid Network for Enhanced Single-Image Super-Resolution with Reduced Computational Requirements
Scale-Equivariant Imaging: Self-Supervised Learning for Image Super-Resolution and Deblurring
Image Restoration Through Generalized Ornstein-Uhlenbeck Bridge
Toward Real World Stereo Image Super-Resolution via Hybrid Degradation Model and Discriminator for Implied Stereo Image Information
EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset
Diffusion-based Blind Text Image Super-Resolution
Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution
Attribute Annotation and Bias Evaluation in Visual Datasets for Autonomous Driving
TULIP: Transformer for Upsampling of LiDAR Point Clouds
Iterative Token Evaluation and Refinement for Real-World Super-Resolution
Multi-strategy Collaborative Optimized YOLOv5s and its Application in Distance Estimation
Training Neural Networks on RAW and HDR Images for Restoration Tasks
Enhancing Vehicle Entrance and Parking Management: Deep Learning Solutions for Efficiency and Security
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.