US 12,445,746 B2
Systems, methods, and media for high dynamic range imaging using single-photon and conventional image sensor data
Felipe Gutierrez Barragan, Alameda, CA (US); Yuhao Liu, Madison, WI (US); Atul Ingle, Madison, WI (US); Mohit Gupta, Madison, WI (US); and Andreas Velten, Madison, WI (US)
Assigned to Wisconsin Alumni Research Foundation, Madison, WI (US)
Filed by Wisconsin Alumni Research Foundation, Madison, WI (US)
Filed on Sep. 11, 2023, as Appl. No. 18/464,987.
Application 18/464,987 is a continuation of application No. 17/572,236, filed on Jan. 10, 2022, granted, now 11,758,297.
Prior Publication US 2024/0259706 A1, Aug. 1, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. H04N 25/585 (2023.01); H10F 39/00 (2025.01); H10F 39/18 (2025.01)
CPC H04N 25/585 (2023.01) [H10F 39/809 (2025.01); H10F 39/182 (2025.01)] 10 Claims
OG exemplary drawing
 
1. A system for generating high dynamic range digital images, comprising:
a communication connection configured to receive readout data from an image data source, the image data source comprising at least one image sensor;
a processor; and
at least one memory connected to receive and store the readout data received by the communication connection, and having stored thereon a set of software instructions which, when executed by the processor, cause the processor to:
receive, via the communication connection, first readout data for a scene, the first readout data indicative of flux detected by a first detector of the at least one image sensor having a first dynamic range and a first resolution;
receive, via the communication connection, second readout data for the scene, the second readout data indicative of flux at a second detector of the image sensor, the second detector having a second dynamic range that is lower than the first dynamic range and a second resolution that is higher than the first resolution;
provide the first readout data and the second readout data as inputs to a trained machine learning model, wherein the trained machine learning model was trained using a dataset comprising image data for scenes with corresponding estimated and known flux values from sensors having different dynamic ranges;
receive, as output from the trained machine learning model, third readout data based on properties of both the first readout data and the second readout data; and
generate an image corresponding to the scene, wherein the image has a third resolution that is higher than the first resolution and a third dynamic range that is higher than the second dynamic range.