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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.
Channel-Partitioned Windowed Attention And Frequency Learning for Single Image Super-Resolution
Efficient Multi-disparity Transformer for Light Field Image Super-resolution
Toward Efficient Convolutional Neural Networks With Structured Ternary Patterns
A New Dataset and Framework for Real-World Blurred Images Super-Resolution
RealViformer: Investigating Attention for Real-World Video Super-Resolution
Large Kernel Distillation Network for Efficient Single Image Super-Resolution
UCIP: A Universal Framework for Compressed Image Super-Resolution using Dynamic Prompt
Research on Image Super-Resolution Reconstruction Mechanism based on Convolutional Neural Network
Backdoor Attacks against Image-to-Image Networks
Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV
Optimizing ROI Benefits Vehicle ReID in ITS
Task-driven single-image super-resolution reconstruction of document scans
Region Attention Transformer for Medical Image Restoration
Spatially-Variant Degradation Model for Dataset-free Super-resolution
Pairwise Distance Distillation for Unsupervised Real-World Image Super-Resolution
UnmixingSR: Material-aware Network with Unsupervised Unmixing as Auxiliary Task for Hyperspectral Image Super-resolution
HiT-SR: Hierarchical Transformer for Efficient Image Super-Resolution
Deform-Mamba Network for MRI Super-Resolution
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution
AnySR: Realizing Image Super-Resolution as Any-Scale, Any-Resource
NSD-DIL: Null-Shot Deblurring Using Deep Identity Learning
ASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolution
Preserving Full Degradation Details for Blind Image Super-Resolution
ASSR-NeRF: Arbitrary-Scale Super-Resolution on Voxel Grid for High-Quality Radiance Fields Reconstruction
Spatial-temporal Hierarchical Reinforcement Learning for Interpretable Pathology Image Super-Resolution
Burst Image Super-Resolution with Base Frame Selection
Improving Generative Adversarial Networks for Video Super-Resolution
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution
DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-Resolution
Learning Accurate and Enriched Features for Stereo Image Super-Resolution
Towards Robust Training Datasets for Machine Learning with Ontologies: A Case Study for Emergency Road Vehicle Detection
Enhance the Image: Super Resolution using Artificial Intelligence in MRI
IG-CFAT: An Improved GAN-Based Framework for Effectively Exploiting Transformers in Real-World Image Super-Resolution
LFMamba: Light Field Image Super-Resolution with State Space Model
A Dictionary Based Approach for Removing Out-of-Focus Blur
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.