从低到超分辨率和更高

C. Kok, Wing-Shan Tam
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引用次数: 0

摘要

本教程从介绍数字图像插值和单图像超分辨率开始。接着定义了各种图像插值性能测量指标,包括客观指标和主观指标。本教程的核心是基于协方差插值的应用,以实现高视觉质量的图像插值和单图像超分辨率结果。将基于协方差的图像边缘插值技术逐层递进到基于协方差校正的迭代图像插值。基于边缘的插值结合了人的视觉系统,获得了视觉愉悦的高分辨率插值结果。在每一层,本教程介绍了每种图像模型和插值技术的优缺点,减轻每种技术的插值视觉伪影的解决方案,以及克服传统边缘定向图像插值技术局限性的创新修改,包括:空间自适应像素强度估计,像素强度校正,误差传播减缓,协方差窗口自适应和迭代协方差校正。本教程将从理论和分析讨论扩展到使用MATLAB的详细实现。观众应该能够带回家实现细节,以及性能和本教程中讨论的插值算法的复杂性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From Low to Super Resolution and Beyond
The tutorial starts with an introduction of digital image interpolation, and single image super-resolution. It continues with the definition of various image interpolation performance measurement indices, including both objective and subjective indices. The core of this tutorial is the application of covariance based interpolation to achieve high visual quality image interpolation and single image super-resolution results. Layer on layer, the covariance based edge-directed image interpolation techniques that makes use of stochastic image model without explicit edge map, to iterative covariance correction based image interpolation. The edge based interpolation incorporated human visual system to achieve visually pleasant high resolution interpolation results. On each layer, the pros and cons of each image model and interpolation technique, solutions to alleviate the interpolation visual artifacts of each techniques, and innovative modification to overcome limitations of traditional edge-directed image interpolation techniques are presented in this tutorial, which includes: spatial adaptive pixel intensity estimation, pixel intensity correction, error propagation mitigation, covariance windows adaptation, and iterative covariance correction. The tutorial will extend from theoretical and analytical discussions to detail implementation using MATLAB. The audience shall be able to bring home with implementation details, as well as the performance and complexity of the interpolation algorithms discussed in this tutorial.
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