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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.
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
Task-Driven Super Resolution: Object Detection in Low-resolution Images
Fast and Accurate Single Image Super-Resolution via Information Distillation Network
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction
Maintaining Natural Image Statistics with the Contextual Loss
Deep Back-Projection Networks For Super-Resolution
Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network
Focal Loss Dense Detector for Vehicle Surveillance
Residual Dense Network for Image Super-Resolution
Single Image Super-Resolution via Cascaded Multi-Scale Cross Network
Detecting Small, Densely Distributed Objects with Filter-Amplifier Networks and Loss Boosting
Scale Optimization for Full-Image-CNN Vehicle Detection
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution Forests
Image Transformer
A General Pipeline for 3D Detection of Vehicles
Deep Image Super Resolution via Natural Image Priors
Orthogonally Regularized Deep Networks For Image Super-resolution
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks
Vehicle Detection in Aerial Images
Frame-Recurrent Video Super-Resolution
Brain MRI Super Resolution Using 3D Deep Densely Connected Neural Networks
High-throughput, high-resolution registration-free generated adversarial network microscopy
Moving Vehicle Detection Using AdaBoost and Haar-Like Feature in Surveillance Videos
3D-DETNet: a Single Stage Video-Based Vehicle Detector
Super-Resolution with Deep Adaptive Image Resampling
"Zero-Shot" Super-Resolution using Deep Internal Learning
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution
Deep Burst Denoising
Image Super-resolution via Feature-augmented Random Forest
A Frequency Domain Neural Network for Fast Image Super-resolution
Image Inpainting for High-Resolution Textures using CNN Texture Synthesis
Super-FAN: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with GANs
Deep Sampling Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.