Wavelet Tree Parsing with Freeform Lensing
Wavelet Tree Parsing with Freeform Lensing
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DOI：
10.1109/iccphot.2019.8747327
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Vishwanath Saragadam;Aswin C. Sankaranarayanan
中科院分区：
文献类型：
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作者：
Vishwanath Saragadam;Aswin C. Sankaranarayanan
我们提出了一个架构，通过逐步测量其小波系数的图像自适应传感。我们的方法，通常被称为小波树解析，自适应地选择特定的小波系数进行感测建模的孩子占主导地位的系数。这种技术的实际实施的一个关键挑战是，小波模式，特别是在更精细的尺度，占据了视场的一小部分，因此，所得到的测量具有非常差的光水平和信噪比（SNR）。为了解决这个问题，我们提出了一种新的成像架构，使用一个相位空间光调制器作为自由曲面透镜集中光源，并创建小波模式。这确保了测量的SNR在不同的空间尺度上保持恒定。使用实验室原型，我们展示了成功的重建广泛的真实的场景，并表明，集中照明使我们能够超越非自适应技术，以及基于传统的投影机的自适应技术。
We propose an architecture for adaptive sensing of images by progressively measuring its wavelet coefficients. Our approach, commonly referred to as wavelet tree parsing, adaptively selects the specific wavelet coefficients to be sensed by modeling the children of dominant coefficients to be dominant themselves. A key challenge for practical implementation of this technique is that the wavelet patterns, especially at finer scales, occupy a tiny portion of the field of view and, hence, the resulting measurements have very poor light levels and signal-to-noise ratios (SNR). To address this, we propose a novel imaging architecture that uses a phase-only spatial light modulator as a freeform lens to concentrate a light source and create the wavelet patterns. This ensures that the SNR of measurements remain constant across different spatial scales. Using a lab prototype, we demonstrate successful reconstruction on a wide range of real scenes and show that concentrating illumination enables us to outperform non-adaptive techniques as well as adaptive techniques based on traditional projectors.