Recent results of medium wave infrared compressive sensing

Area of Science:

• Optics and Photonics
• Signal Processing
• Infrared Technology

Background:

• Compressive sensing (CS) theory leverages signal sparsity for high-resolution recovery from limited measurements.
• Conventional imaging sensors often require numerous detectors, increasing cost and size, particularly for infrared (IR) applications.
• CS offers a potential solution to reduce hardware complexity and enhance IR imaging systems.

Purpose of the Study:

• To experimentally validate the effectiveness of compressive sensing for recovering high-resolution mid-wave infrared (MWIR) images.
• To demonstrate the feasibility of using smaller focal plane arrays (FPAs) enabled by CS technology.
• To explore the system-level benefits and practical challenges of CS in infrared imaging.

Main Methods:

• Development and utilization of a compressive sensing testbed for MWIR imaging.
• Implementation using a reflective spatial light modulator and a variable-pixel-size focal plane array.
• Image reconstruction algorithms applied to low-resolution measurements to recover high-resolution data.

Main Results:

• Successful experimental recovery of high-resolution spatial information from low-resolution measurements using the CS testbed.
• Demonstration of the testbed's operation and image reconstruction capabilities.
• Confirmation that CS can yield high-resolution data for tasks like imaging and target detection.

Conclusions:

• Compressive sensing is a viable technique for achieving high-resolution infrared imaging with reduced hardware requirements.
• CS technology has significant potential to lower the cost and footprint of infrared sensors.
• Further research is needed to address practical device limitations and sensor calibration for future CS infrared imagers.