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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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Hyperspectral Image Super Resolution with Real Unaligned RGB Guidance
Hypernetworks build Implicit Neural Representations of Sounds
OSRT: Omnidirectional Image Super-Resolution with Distortion-aware Transformer
A statistically constrained internal method for single image super-resolution
Fisheye traffic data set of point center markers
Image Restoration with Mean-Reverting Stochastic Differential Equations
Trainable Loss Weights in Super-Resolution
Image Super-Resolution using Efficient Striped Window Transformer
Is Autoencoder Truly Applicable for 3D CT Super-Resolution?
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution
CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution
Deep Residual Axial Networks
DLGSANet: Lightweight Dynamic Local and Global Self-Attention Networks for Image Super-Resolution
A Fine-Grained Vehicle Detection (FGVD) Dataset for Unconstrained Roads
Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network
High-temporal-resolution event-based vehicle detection and tracking
Single-Image Super-Resolution Reconstruction based on the Differences of Neighboring Pixels
Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images
Infrared Image Super-Resolution: Systematic Review, and Future Trends
Multi-Reference Image Super-Resolution: A Posterior Fusion Approach
Reference-based Image and Video Super-Resolution via C2-Matching
Performance Analysis of YOLO-based Architectures for Vehicle Detection from Traffic Images in Bangladesh
DCS-RISR: Dynamic Channel Splitting for Efficient Real-world Image Super-Resolution
Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation
Automatic vehicle trajectory data reconstruction at scale
U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image Fusion
A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information
CiaoSR: Continuous Implicit Attention-in-Attention Network for Arbitrary-Scale Image Super-Resolution
Learning Detail-Structure Alternative Optimization for Blind Super-Resolution
Bridging Component Learning with Degradation Modelling for Blind Image Super-Resolution
Global Learnable Attention for Single Image Super-Resolution
Domain Adaptive Scene Text Detection via Subcategorization
Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
FREDSR: Fourier Residual Efficient Diffusive GAN for Single Image Super Resolution
From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-Resolution
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