https://github.com/fannymonori/TF-LapSRN
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Image Super-Resolution Using Very Deep Residual Channel Attention Networks
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
A Fully Progressive Approach to Single-Image Super-Resolution
SRFlow: Learning the Super-Resolution Space with Normalizing Flow
Multimodal Sensor Fusion In Single Thermal image Super-Resolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Advanced Super-Resolution using Lossless Pooling Convolutional Networks
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
DroNet: Efficient convolutional neural network detector for real-time UAV applications
DroNet: Efficient convolutional neural network detector for real-time UAV applications
Binary Document Image Super Resolution for Improved Readability and OCR Performance
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
SDT-DCSCN for Simultaneous Super-Resolution and Deblurring of Text Images
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.