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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.
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Parking Analytics Framework using Deep Learning
GCFSR: a Generative and Controllable Face Super Resolution Method Without Facial and GAN Priors
Efficient Long-Range Attention Network for Image Super-resolution
Unfolded Deep Kernel Estimation for Blind Image Super-resolution
Learning the Degradation Distribution for Blind Image Super-Resolution
Adaptive Cross-Layer Attention for Image Restoration
Fast Neural Architecture Search for Lightweight Dense Prediction Networks
Dense Voxel Fusion for 3D Object Detection
Towards Bidirectional Arbitrary Image Rescaling: Joint Optimization and Cycle Idempotence
Conditional Simulation Using Diffusion Schrödinger Bridges
Multi-image Super-resolution via Quality Map Associated Attention Network
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-Resolution
Single Image Super-Resolution Methods: A Survey
Ensemble Learning techniques for object detection in high-resolution satellite images
Deep Constrained Least Squares for Blind Image Super-Resolution
Memory-augmented Deep Unfolding Network for Guided Image Super-resolution
Trained Model in Supervised Deep Learning is a Conditional Risk Minimizer
Patch-Based Stochastic Attention for Image Editing
TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With Transformers
Gradient Variance Loss for Structure-Enhanced Image Super-Resolution
An Optimal Transport Perspective on Unpaired Image Super-Resolution
Self-Supervised Moving Vehicle Detection from Audio-Visual Cues
Potential destination discovery for low predictability individuals based on knowledge graph
Deep Networks for Image and Video Super-Resolution
Image Superresolution using Scale-Recurrent Dense Network
Revisiting RCAN: Improved Training for Image Super-Resolution
Learning Multiple Probabilistic Degradation Generators for Unsupervised Real World Image Super Resolution
Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion
Robust Unpaired Single Image Super-Resolution of Faces
A Review of Deep Learning Based Image Super-resolution Techniques
Improving Clinical Diagnosis Performance with Automated X-ray Scan Quality Enhancement Algorithms
Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN
CISRNet: Compressed Image Super-Resolution Network
SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.