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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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Process of image super-resolution
Super Resolution Convolutional Neural Network Models for Enhancing Resolution of Rock Micro-CT Images
A Deep Journey into Super-resolution: A survey
Evaluating Robustness of Deep Image Super-Resolution against Adversarial Attacks
MAANet: Multi-view Aware Attention Networks for Image Super-Resolution
Difficulty-aware Image Super Resolution via Deep Adaptive Dual-Network
Blind Super-Resolution With Iterative Kernel Correction
Camera Lens Super-Resolution
Controlling Neural Networks via Energy Dissipation
Lightweight Image Super-Resolution with Adaptive Weighted Learning Network
Deep Back-Projection Networks for Single Image Super-resolution
Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model
PIRM2018 Challenge on Spectral Image Super-Resolution: Dataset and Study
CFSNet: Toward a Controllable Feature Space for Image Restoration
Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels
Recurrent Back-Projection Network for Video Super-Resolution
DeepRED: Deep Image Prior Powered by RED
Feedback Network for Image Super-Resolution
Fast Bayesian Uncertainty Estimation and Reduction of Batch Normalized Single Image Super-Resolution Network
A Matrix-in-matrix Neural Network for Image Super Resolution
Proximal Splitting Networks for Image Restoration
Flickr1024: A Large-Scale Dataset for Stereo Image Super-Resolution
Learning Parallax Attention for Stereo Image Super-Resolution
Universally Slimmable Networks and Improved Training Techniques
Efficient Deep Neural Network for Photo-realistic Image Super-Resolution
Image Super-Resolution by Neural Texture Transfer
Meta-SR: A Magnification-Arbitrary Network for Super-Resolution
Deep Learning for Multiple-Image Super-Resolution
Hypernetwork functional image representation
Generative Collaborative Networks for Single Image Super-Resolution
PIXOR: Real-time 3D Object Detection from Point Clouds
Deep Learning for Image Super-resolution: A Survey
Lightweight Feature Fusion Network for Single Image Super-Resolution
Super-Resolution of Brain MRI Images using Overcomplete Dictionaries and Nonlocal Similarity
Progressive Generative Adversarial Networks for Medical Image Super resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.