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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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이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Provident Vehicle Detection at Night: The PVDN Dataset
Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGAN
Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks
Computer Vision based Accident Detection for Autonomous Vehicles
Neural Radiance Flow for 4D View Synthesis and Video Processing
Attention-based Image Upsampling
Learning-Based Quality Assessment for Image Super-Resolution
Learning Continuous Image Representation with Local Implicit Image Function
Learning Omni-frequency Region-adaptive Representations for Real Image Super-Resolution
Super-resolution Guided Pore Detection for Fingerprint Recognition
Generative Data Augmentation for Vehicle Detection in Aerial Images
Bayesian Image Reconstruction using Deep Generative Models
Hierarchical Residual Attention Network for Single Image Super-Resolution
Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors
Learning Spatial Attention for Face Super-Resolution
Pre-Trained Image Processing Transformer
GLEAN: Generative Latent Bank for Large-Factor Image Super-Resolution
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images
Fully Quantized Image Super-Resolution Networks
Single Image Super-resolution with a Switch Guided Hybrid Network for Satellite Images
Rank-One Network: An Effective Framework for Image Restoration
Multi-Scale Progressive Fusion Learning for Depth Map Super-Resolution
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning
Interpreting Super-Resolution Networks with Local Attribution Maps
On-Device Text Image Super Resolution
Exploring intermediate representation for monocular vehicle pose estimation
Fast and Robust Cascade Model for Multiple Degradation Single Image Super-Resolution
Lightweight Single-Image Super-Resolution Network with Attentive Auxiliary Feature Learning
Dense U-net for super-resolution with shuffle pooling layer
Optimized Loss Functions for Object detection: A Case Study on Nighttime Vehicle Detection
Strict Enforcement of Conservation Laws and Invertibility in CNN-Based Super Resolution for Scientific Datasets
LittleYOLO-SPP: A Delicate Real-Time Vehicle Detection Algorithm
Joint Super-Resolution and Rectification for Solar Cell Inspection
Fine Perceptive GANs for Brain MR Image Super-Resolution in Wavelet Domain
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.