HDR Imaging From Quantization Noise
Ayush Bhandari, Felix Krahmer
Abstract
Quantization is an integral part of image acquisition but also a major performance bottleneck due to the trade-off between dynamic range and resolution. As we discuss in this paper, in contrast, quantization noise can be acquired reliably even beyond the dynamic range by re-purposing recent hardware development. In this paper, we introduce and mathematically analyze an algorithm to recover images from this information, thus giving rise to a novel, single-shot, high-dynamic-range (HDR) imaging approach. Our method directly works with a refined model for sensor outputs at the digitization stage and crucially exploits smoothing anti-aliasing artifacts. We derive recovery guarantees and demonstrate the validity of our approach via computer experiments. Our work suggests re-thinking of the imaging pipeline as seeming sensing artifacts can lead to improved reconstruction when combined with proper computational methodology.