论文标题

Vevid:通过虚拟衍射和相干检测来增强视力

VEViD: Vision Enhancement via Virtual diffraction and coherent Detection

论文作者

MacPhee, Callen, Jalali, Bahram

论文摘要

计算的历史记录始于由执行专业功能的物理设备组成的模拟计算机,例如预测大炮球的轨迹。在现代,例如,扩展了这个想法,例如,超快的非线性光学元件用作替代模拟计算机,以探测复杂现象(例如流氓波)的行为。在这里,我们讨论了一个新的范式,其中物理现象被编码为算法执行计算成像任务。具体而言,衍射后面是相干检测,而不是在模拟实现中,而是在编码为算法时成为图像增强工具。通过虚拟衍射和相干检测(VEVID)提出的视觉增强此处引入的数字图像将数字图像重新构想为空间变化的隐喻光场,然后将场受到类似于衍射和相干检测的物理过程。 “虚拟”一词捕获了与物理世界的偏差。光场被像素化,传播赋予一个随意依赖频率的相位,这可能与物理衍射的二次行为不同。时间频率存在于与数字图像的RGB颜色通道相对应的三个频段中。输出的相位而非强度代表输出图像。 Vevid是一种高性能的低光层和颜色增强工具,它从此范式中出现。该算法是可解释的,并且在计算上有效。我们以每秒200帧的速度展示了4K视频的图像增强,并显示了该物理算法在提高神经网络对象检测准确性时的实用性,而无需在低光条件下重新训练模型。还展示了Vevid在颜色增强上的应用。

The history of computing started with analog computers consisting of physical devices performing specialized functions such as predicting the trajectory of cannon balls. In modern times, this idea has been extended, for example, to ultrafast nonlinear optics serving as a surrogate analog computer to probe the behavior of complex phenomena such as rogue waves. Here we discuss a new paradigm where physical phenomena coded as an algorithm perform computational imaging tasks. Specifically, diffraction followed by coherent detection, not in its analog realization but when coded as an algorithm, becomes an image enhancement tool. Vision Enhancement via Virtual diffraction and coherent Detection (VEViD) introduced here reimagines a digital image as a spatially varying metaphoric light field and then subjects the field to the physical processes akin to diffraction and coherent detection. The term "Virtual" captures the deviation from the physical world. The light field is pixelated and the propagation imparts a phase with an arbitrary dependence on frequency which can be different from the quadratic behavior of physical diffraction. Temporal frequencies exist in three bands corresponding to the RGB color channels of a digital image. The phase of the output, not the intensity, represents the output image. VEViD is a high-performance low-light-level and color enhancement tool that emerges from this paradigm. The algorithm is interpretable and computationally efficient. We demonstrate image enhancement of 4k video at 200frames per second and show the utility of this physical algorithm in improving the accuracy of object detection by neural networks without having to retrain model for low-light conditions. The application of VEViD to color enhancement is also demonstrated.

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