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Image Super-Resolution
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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.
Multi-granularity Backprojection Transformer for Remote Sensing Image Super-Resolution
Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach
Video Super-Resolution Using a Grouped Residual in Residual Network
Image super-resolution via dynamic network
Learning Many-to-Many Mapping for Unpaired Real-World Image Super-resolution and Downscaling
MSight: An Edge-Cloud Infrastructure-based Perception System for Connected Automated Vehicles
A Deeply Supervised Semantic Segmentation Method Based on GAN
Degradation-Aware Self-Attention Based Transformer for Blind Image Super-Resolution
Deep learning-based image super-resolution of a novel end-expandable optical fiber probe for application in esophageal cancer diagnostics
EXTRACTER: Efficient Texture Matching with Attention and Gradient Enhancing for Large Scale Image Super Resolution
Steered Diffusion: A Generalized Framework for Plug-and-Play Conditional Image Synthesis
Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN
Multi-Depth Branch Network for Efficient Image Super-Resolution
Prior Mismatch and Adaptation in PnP-ADMM with a Nonconvex Convergence Analysis
Data Upcycling Knowledge Distillation for Image Super-Resolution
Adaptation of the super resolution SOTA for Art Restoration in camera capture images
Cine cardiac MRI reconstruction using a convolutional recurrent network with refinement
License Plate Super-Resolution Using Diffusion Models
Frequency Estimation Using Complex-Valued Shifted Window Transformer
Emerging Approaches for THz Array Imaging: A Tutorial Review and Software Tool
Pixel Adapter: A Graph-Based Post-Processing Approach for Scene Text Image Super-Resolution
MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces
An Efficient Wide-Range Pseudo-3D Vehicle Detection Using A Single Camera
Polygon Intersection-over-Union Loss for Viewpoint-Agnostic Monocular 3D Vehicle Detection
RGB-Guided Resolution Enhancement of IR Images
Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration
HAT: Hybrid Attention Transformer for Image Restoration
Towards Real-World Burst Image Super-Resolution: Benchmark and Method
SKoPe3D: A Synthetic Dataset for Vehicle Keypoint Perception in 3D from Traffic Monitoring Cameras
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization
Diffusion Models for Image Restoration and Enhancement -- A Comprehensive Survey
SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments
Dynamic Attention-Guided Diffusion for Image Super-Resolution
CMISR: Circular Medical Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.