Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
87 tokens/sec
Gemini 2.5 Pro Premium
36 tokens/sec
GPT-5 Medium
31 tokens/sec
GPT-5 High Premium
39 tokens/sec
GPT-4o
95 tokens/sec
DeepSeek R1 via Azure Premium
91 tokens/sec
GPT OSS 120B via Groq Premium
460 tokens/sec
Kimi K2 via Groq Premium
219 tokens/sec
2000 character limit reached

Signal-to-noise ratio analysis of single-pixel detection multiplexing under photon-noise. Cases of Hadamard and Cosine positive modulation (2204.06308v1)

Published 13 Apr 2022 in physics.optics, physics.app-ph, and physics.data-an

Abstract: In typical single-pixel detection multiplexing, an unknown object is sequentially illuminated with intensity patterns: the total signal is summed into a single-pixel detector and is then demultiplexed to retrieve the object. Because of measurement noise, the retrieved object differs from the ground truth by some error quantified by the signal-to-noise ratio (SNR). In situations where the noise only arises from the photon counting process, it has not been made clear if single-pixel detection multiplexing leads to a better SNR than simply scanning the object with a focused intensity spot - a modality known as raster scanning. This study theoretically assesses the SNR associated with certain types of single-pixel detection multiplexing, and compares it with raster scanning. In particular, we show that, under photon noise, when the positive intensity modulation is based on Hadamard or Cosine patterns, single-pixel detection multiplexing does not systematically improve the SNR as compared to raster scanning. Instead, it only improves the SNR on object pixels at least $k$ times brighter than the object mean signal $\bar{x}$, where $k$ is a constant that depends on the modulation scheme.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.

Don't miss out on important new AI/ML research

See which papers are being discussed right now on X, Reddit, and more:

“Emergent Mind helps me see which AI papers have caught fire online.”

Philip

Philip

Creator, AI Explained on YouTube