Wavelet Tree Parsing with Freeform Lensing

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.