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首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Enhancing resolution along multiple imaging dimensions using assorted pixels
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Enhancing resolution along multiple imaging dimensions using assorted pixels

机译：使用分类的像素沿多个成像维度增强分辨率

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摘要

Multisampled imaging is a general framework for using pixels on an image detector to simultaneously sample multiple dimensions of imaging (space, time, spectrum, brightness, polarization, etc.). The mosaic of red, green, and blue spectral filters found in most solid-state color cameras is one example of multisampled imaging. We briefly describe how multisampling can be used to explore other dimensions of imaging. Once such an image is captured, smooth reconstructions along the individual dimensions can be obtained using standard interpolation algorithms. Typically, this results in a substantial reduction of resolution (and, hence, image quality). One can extract significantly greater resolution in each dimension by noting that the light fields associated with real scenes have enormous redundancies within them, causing different dimensions to be highly correlated. Hence, multisampled images can be better interpolated using local structural models that are learned offline from a diverse set of training images. The specific type of structural models we use are based on polynomial functions of measured image intensities. They are very effective as well as computationally efficient. We demonstrate the benefits of structural interpolation using three specific applications. These are 1) traditional color imaging with a mosaic of color filters, 2) high dynamic range monochrome imaging using a mosaic of exposure filters, and 3) high dynamic range color imaging using a mosaic of overlapping color and exposure filters.
机译：多采样成像是用于使用图像检测器上的像素同时采样成像的多个维度（空间，时间，光谱，亮度，偏振等）的通用框架。在大多数固态彩色相机中发现的红色，绿色和蓝色光谱滤镜的马赛克是多采样成像的一个例子。我们简要描述了如何使用多重采样来探索成像的其他维度。一旦捕获了此类图像，就可以使用标准插值算法获得沿各个维度的平滑重建。通常，这会导致分辨率大大降低（并因此降低图像质量）。通过注意到与真实场景关联的光场在其中具有巨大的冗余度，从而使不同的维度高度相关，可以在每个维度上提取出更高的分辨率。因此，使用从多种训练图像中离线学习的局部结构模型可以更好地插值多采样图像。我们使用的特定类型的结构模型基于测得图像强度的多项式函数。它们非常有效，而且计算效率很高。我们使用三个特定的应用程序演示了结构插值的好处。这些是1）使用彩色滤光片马赛克的传统彩色成像； 2）使用曝光滤光片马赛克的高动态范围单色成像，以及3）使用重叠色彩和曝光滤光片马赛克的高动态范围彩色成像。

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