RTS noise and dark current white defects reduction using selective averaging based on a multi-aperture system.

Area of Science:

• Image processing and sensor technology
• Low-light imaging systems
• Noise reduction techniques

Background:

• Extremely low-light conditions present challenges due to visible random telegraph signal (RTS) noise and dark current white defects.
• Existing imaging systems struggle to capture clear images in such demanding environments.

Purpose of the Study:

• To propose a multi-aperture imaging system and selective averaging method to effectively remove RTS noise and dark current defects.
• To minimize synthetic sensor noise at each pixel for improved image quality.
• To enhance the peak signal-to-noise ratio (PSNR) in low-light imaging.

Main Methods:

• Development of a multi-aperture imaging system achieving a synthetic F-number less than 1.0 through increased optical gain.
• Implementation of a selective averaging method to reduce noise.
• Utilization of low-noise CMOS image sensors with folding-integration and cyclic column ADCs.
• Experimental validation using a prototype 3x3-aperture camera.

Main Results:

• Simulation showed a reduction in effective noise normalized by optical gain from 1.38e⁻ to 0.48 e⁻ in a 3x3-aperture system.
• Experimental results demonstrated the removal of RTS and dark current white defects under low-light conditions (max average signal of 11e⁻ per aperture).
• A significant increase of 6.3 dB in peak signal-to-noise ratio (PSNR) was achieved.

Conclusions:

• The proposed multi-aperture imaging system and selective averaging method are effective in removing noise and defects in extremely low-light conditions.
• The system significantly improves image quality, as evidenced by the substantial increase in PSNR.
• This technology offers a viable solution for high-quality imaging in challenging low-light environments.