Objective detection for boundary noise of low-light-level image intensifiers based on Gaussian differential filtering and shape similarity calculation

Key Points

• Boundary noise in low-light-level images can impact visual performance, and detection methods are critical.
• Gaussian differential filtering effectively identifies boundary noise, enhancing image quality in low-light environments.
• Shape similarity calculations are employed to assess and reduce noise interference across varying image intensifiers.
• Improved noise detection techniques may lead to advancements in visual technologies, highlighting the need for further development.