基于实例的超分辨率优化物理预处理

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摘要翻译：
在基于示例的超分辨率中，低分辨率图像与高分辨率图像之间的函数是从给定的数据集中学习的。这种数据驱动的解决提高图像分辨率的逆问题的方法已经用深度学习算法实现。在这项工作中，我们探索修改成像硬件，以收集更多信息的低分辨率图像，从而更好地最终重建高分辨率图像。我们表明，这种“物理预处理”允许在傅立叶指纹显微镜中用深度学习改进图像重建。傅里叶显微技术是一种以时间分辨率为代价实现高分辨率和高视场的技术。在傅里叶显微技术中，可变的照明模式被用来收集多个低分辨率图像。然后，这些低分辨率图像被计算组合，以创建分辨率超过显微镜中任何单个图像的图像。我们使用深度学习来联合优化光照模式和给定样本类型的后处理重建算法，允许同时具有高分辨率和高视场的单次拍摄成像。我们用仿真数据证明，与单一的后处理重建算法相比，联合优化产生了更好的图像重建效果。
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英文标题：
《Optimal Physical Preprocessing for Example-Based Super-Resolution》
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作者：
Alexander Robey and Vidya Ganapati
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  In example-based super-resolution, the function relating low-resolution images to their high-resolution counterparts is learned from a given dataset. This data-driven approach to solving the inverse problem of increasing image resolution has been implemented with deep learning algorithms. In this work, we explore modifying the imaging hardware in order to collect more informative low-resolution images for better ultimate high-resolution image reconstruction. We show that this "physical preprocessing" allows for improved image reconstruction with deep learning in Fourier ptychographic microscopy.   Fourier ptychographic microscopy is a technique allowing for both high resolution and high field-of-view at the cost of temporal resolution. In Fourier ptychographic microscopy, variable illumination patterns are used to collect multiple low-resolution images. These low-resolution images are then computationally combined to create an image with resolution exceeding that of any single image from the microscope. We use deep learning to jointly optimize the illumination pattern with the post-processing reconstruction algorithm for a given sample type, allowing for single-shot imaging with both high resolution and high field-of-view. We demonstrate, with simulated data, that the joint optimization yields improved image reconstruction as compared with sole optimization of the post-processing reconstruction algorithm.
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PDF链接：
https://arxiv.org/pdf/1807.04813

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关键词：分辨率 预处理 Construction Optimization Mathematical 数据 分辨率 视场 图像 images