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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
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An Empirical Evaluation of Deep Learning on Highway Driving
Single image super-resolution by approximated Heaviside functions
Learning to Detect Vehicles by Clustering Appearance Patterns
Learning Super-Resolution Jointly from External and Internal Examples
Image Super-Resolution Using Deep Convolutional Networks
Higher-order MRFs based image super resolution: why not MAP?
Vehicle Detection and Tracking Techniques: A Concise Review
Single Image Super Resolution via Manifold Approximation
A Reverse Hierarchy Model for Predicting Eye Fixations
Structure Tensor Based Image Interpolation Method
Sparse Coding Approach for Multi-Frame Image Super Resolution
Single image super resolution in spatial and wavelet domain
A Convex Approach for Image Hallucination
Analysis Operator Learning and Its Application to Image Reconstruction
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