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Creators/Authors contains: "Saragadam, Vishwanath"

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  1. Free, publicly-accessible full text available January 1, 2024
  2. We introduce DeepIR, a new thermal image processing framework that combines physically accurate sensor modeling with deep network-based image representation. Our key enabling observations are that the images captured by thermal sensors can be factored into slowly changing, scene-independent sensor non-uniformities (that can be accurately modeled using physics) and a scene-specific radiance flux (that is well-represented using a deep network-based regularizer). DeepIR requires neither training data nor periodic ground-truth calibration with a known black body target--making it well suited for practical computer vision tasks. We demonstrate the power of going DeepIR by developing new denoising and super-resolution algorithms that exploit multiple images of the scene captured with camera jitter. Simulated and real data experiments demonstrate that DeepIR can perform high-quality non-uniformity correction with as few as three images, achieving a 10dB PSNR improvement over competing approaches.
  3. 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.