Depth of field is an important factor of imaging systems that highly affects the quality of the acquired spatial information. Extended depth of field (EDoF) imaging is a challenging ill-posed problem and has been extensively addressed in the literature. We propose a computational imaging approach for EDoF, where we employ wavefront coding via a diffractive optical element (DOE) and we achieve deblurring through a convolutional neural network. Thanks to the end-to-end differentiable modeling of optical image formation and computational post-processing, we jointly optimize the optical design, i.e., DOE, and the deblurring through standard gradient descent methods. Based on the properties of the underlying refractive lens and the desired EDoF range, we provide an analytical expression for the search space of the DOE, which is instrumental in the convergence of the end-to-end network. We achieve superior EDoF imaging performance compared to the state of the art, where we demonstrate results with minimal artifacts in various scenarios, including deep 3D scenes and broadband imaging.

中文翻译：

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学习波前编码以扩展景深成像

景深是成像系统的重要因素，它会严重影响所获取空间信息的质量。扩展景深（EF）成像是一个具有挑战性的不适定问题，并且在文献中已得到广泛解决。我们提出了一种电子成像的计算成像方法，该方法通过衍射光学元件（DOE）进行波前编码，并通过卷积神经网络实现去模糊。得益于光学图像形成和计算后处理的端到端微分建模，我们共同优化了光学设计（即DOE），并通过标准梯度下降方法对图像进行了去模糊处理。根据下面的折射透镜的特性和所需的eF范围，我们为DOE的搜索空间提供了一个解析表达式，这有助于端到端网络的融合。与现有技术相比，我们实现了卓越的eF成像性能，在现有技术中，我们在各种场景（包括深3D场景和宽带成像）中以最少的伪影演示了结果。