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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
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Roadside Lidar Vehicle Detection and Tracking Using Range And Intensity Background Subtraction
Flexible Style Image Super-Resolution using Conditional Objective
Efficient Non-Local Contrastive Attention for Image Super-Resolution
Uncovering the Over-smoothing Challenge in Image Super-Resolution: Entropy-based Quantification and Contrastive Optimization
3DVSR: 3D EPI Volume-based Approach for Angular and Spatial Light field Image Super-resolution
Detail-Preserving Transformer for Light Field Image Super-Resolution
Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning
Astronomical Image Colorization and upscaling with Generative Adversarial Networks
DSRGAN: Detail Prior-Assisted Perceptual Single Image Super-Resolution via Generative Adversarial Networks
Reflash Dropout in Image Super-Resolution
Implicit Neural Representation Learning for Hyperspectral Image Super-Resolution
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net Discriminators
On Efficient Transformer-Based Image Pre-training for Low-Level Vision
Feature Distillation Interaction Weighting Network for Lightweight Image Super-Resolution
Kernel-aware Burst Blind Super-Resolution
Mitigating Channel-wise Noise for Single Image Super Resolution
SphereSR: 360° Image Super-Resolution with Arbitrary Projection via Continuous Spherical Image Representation
Text Gestalt: Stroke-Aware Scene Text Image Super-Resolution
Enhancing Multi-Scale Implicit Learning in Image Super-Resolution with Integrated Positional Encoding
A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution
PP-MSVSR: Multi-Stage Video Super-Resolution
Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image Fusion
T-YOLO: Tiny Vehicle Detection Based on YOLO and Multi-Scale Convolutional Neural Networks
SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution
SwiftSRGAN -- Rethinking Super-Resolution for Efficient and Real-time Inference
Performance of a GPU- and Time-Efficient Pseudo 3D Network for Magnetic Resonance Image Super-Resolution and Motion Artifact Reduction
AdaDM: Enabling Normalization for Image Super-Resolution
Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution
A Practical Contrastive Learning Framework for Single-Image Super-Resolution
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective
Local-Selective Feature Distillation for Single Image Super-Resolution
FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform
Local Texture Estimator for Implicit Representation Function
Image Super-Resolution Using T-Tetromino Pixels
Fine-Grained Vehicle Classification in Urban Traffic Scenes using Deep Learning
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