Spectral sensitivity is largely related to sensor imaging, which has drawn widespread attention in computer vision. Accurate estimation becomes increasingly urgent because manufacturers rarely disclose it. In this Letter, we present a novel, compact, inexpensive, and real-time computational system for snapshot spectral sensitivity estimation. A multi-scale camera based on the multi-scale convolutional neural network is first proposed, to the best of our knowledge, to automatically extract multiplexing features of an input image by multiscale deep learning, which is vital to solving the inverse problem in sensitivity estimation. Our network is flexible and can be designed with different convolutional kernel sizes for a given application. We build a dataset with 10,500 raw images and generate an excellent pre-trained model. Commercial cameras are adopted to test model validity; the results show that our system can achieve estimation accuracy as high as 91.35%. We provide a method for system design, propose a deep learning network, build a dataset, demonstrate training process, and present experimental results with high precision. This simple and effective method provides an accurate approach for precise estimation of spectral sensitivity and is suitable for computational applications such as pathological digital stain, virtual/augmented reality display, and high-quality image acquisition.

中文翻译：

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用于快照相机光谱灵敏度估计的免扫描端到端新方法

光谱灵敏度很大程度上与传感器成像有关，这在计算机视觉领域引起了广泛关注。由于制造商很少公开，准确估计变得越来越紧迫。在这封信中，我们提出了一种新颖、紧凑、廉价且实时的计算系统，用于快照光谱灵敏度估计。据我们所知，首先提出了一种基于多尺度卷积神经网络的多尺度相机，通过多尺度深度学习自动提取输入图像的复用特征，这对于解决灵敏度估计中的逆问题至关重要. 我们的网络很灵活，可以针对给定的应用程序设计不同的卷积核大小。我们构建了一个包含 10,500 个原始图像的数据集，并生成了一个出色的预训练模型。采用商用相机测试模型有效性；结果表明，我们的系统可以达到高达 91.35% 的估计准确率。我们提供系统设计方法，提出深度学习网络，构建数据集，演示训练过程，并以高精度呈现实验结果。这种简单有效的方法为精确估计光谱灵敏度提供了一种准确的方法，适用于病理数字染色、虚拟/增强现实显示和高质量图像采集等计算应用。并以高精度呈现实验结果。这种简单有效的方法为精确估计光谱灵敏度提供了一种准确的方法，适用于病理数字染色、虚拟/增强现实显示和高质量图像采集等计算应用。并以高精度呈现实验结果。这种简单有效的方法为精确估计光谱灵敏度提供了一种准确的方法，适用于病理数字染色、虚拟/增强现实显示和高质量图像采集等计算应用。