Optical blur is an inherent property of any lens system and is challenging to
model in modern cameras because of their complex optical elements. To tackle
this challenge, we introduce a high-dimensional neural representation of
blur$-$$\textit{the lens blur field}$$-$and a practical method for acquiring
it. The lens blur field is a multilayer perceptron (MLP) designed to (1)
accurately capture variations of the lens 2D point spread function over image
plane location, focus setting and, optionally, depth and (2) represent these
variations parametrically as a single, sensor-specific function. The
representation models the combined effects of defocus, diffraction, aberration,
and accounts for sensor features such as pixel color filters and pixel-specific
micro-lenses. To learn the real-world blur field of a given device, we
formulate a generalized non-blind deconvolution problem that directly optimizes
the MLP weights using a small set of focal stacks as the only input. We also
provide a first-of-its-kind dataset of 5D blur fields$-$for smartphone cameras,
camera bodies equipped with a variety of lenses, etc. Lastly, we show that
acquired 5D blur fields are expressive and accurate enough to reveal, for the
first time, differences in optical behavior of smartphone devices of the same
make and model.

通过引入高维度的神经模型 ——{lens blur field} 和一种实用的获取方法，我们成功解决了现代相机中复杂光学元素导致的光学模糊的建模难题，并展示了通过获取 5D 模糊领域可以揭示出同款智能手机设备的光学行为差异的现象。

学习镜头模糊场

Learning Lens Blur Fields

State-of-the-art algorithms for many semantic visual tasks are based on the
use of convolutional neural networks. These networks are commonly trained, and
evaluated, on large annotated datasets of artifact-free high-quality images. In
this paper, we investigate the effect of one such artifact that is quite common
in natural capture settings: optical blur. We show that standard network
models, trained only on high-quality images, suffer a significant degradation
in performance when applied to those degraded by blur due to defocus, or
subject or camera motion. We investigate the extent to which this degradation
is due to the mismatch between training and input image statistics.
Specifically, we find that fine-tuning a pre-trained model with blurred images
added to the training set allows it to regain much of the lost accuracy. We
also show that there is a fair amount of generalization between different
degrees and types of blur, which implies that a single network model can be
used robustly for recognition when the nature of the blur in the input is
unknown. We find that this robustness arises as a result of these models
learning to generate blur invariant representations in their hidden layers. Our
findings provide useful insights towards developing vision systems that can
perform reliably on real world images affected by blur.

本文探讨使用模糊图像的语义视觉任务中对基于卷积神经网络的模型的影响以及如何通过对预训练模型进行微调并加入模糊图像进行训练来提高模型精度，发现模型在隐藏层中学习生成模糊不变表示并且具有很强的模糊泛化能力，为开发实现在受模糊影响的实际世界图像上可靠操作的视觉系统提供有用的见解。