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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.
Knowledge Distillation based Degradation Estimation for Blind Super-Resolution
MrSARP: A Hierarchical Deep Generative Prior for SAR Image Super-resolution
Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution
Synthetic Low-Field MRI Super-Resolution Via Nested U-Net Architecture
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis
GAN Prior based Null-Space Learning for Consistent Super-Resolution
Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation
SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution
N-Gram in Swin Transformers for Efficient Lightweight Image Super-Resolution
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems
Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network
Semantic Encoder Guided Generative Adversarial Face Ultra-Resolution Network
RDRN: Recursively Defined Residual Network for Image Super-Resolution
Super-resolution Reconstruction of Single Image for Latent features
CurvPnP: Plug-and-play Blind Image Restoration with Deep Curvature Denoiser
A Comprehensive Survey of Transformers for Computer Vision
Contrastive Learning for Climate Model Bias Correction and Super-Resolution
The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks
RRSR:Reciprocal Reference-based Image Super-Resolution with Progressive Feature Alignment and Selection
Underwater Image Super-Resolution using Generative Adversarial Network-based Model
Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report
Measurement-Consistent Networks via a Deep Implicit Layer for Solving Inverse Problems
Mixture-Net: Low-Rank Deep Image Prior Inspired by Mixture Models for Spectral Image Recovery
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks
Fine-tuned Generative Adversarial Network-based Model for Medical Image Super-Resolution
Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution
Iris super-resolution using CNNs: is photo-realism important to iris recognition?
PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds
Single Image Super-Resolution via a Dual Interactive Implicit Neural Network
How Real is Real: Evaluating the Robustness of Real-World Super Resolution
Self-Supervised Pretraining on Satellite Imagery: a Case Study on Label-Efficient Vehicle Detection
Boomerang: Local sampling on image manifolds using diffusion models
Single Image Super-Resolution Using Lightweight Networks Based on Swin Transformer
A Real-Time Wrong-Way Vehicle Detection Based on YOLO and Centroid Tracking
Real Image Super-Resolution using GAN through modeling of LR and HR process
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