Algorithmic Uncertainty Bounds for Iterative Phase Retrieval in Stress Birefringent Polymer Devices
Algorithmic uncertainty bounds define exact confidence intervals for phase retrieval, preventing false passes in stress birefringent polymer optics.
Spatial Aliasing
Meaning
Sampling defects arise when the frequency of a pattern in the object exceeds the resolution of the sensor array. Spatial aliasing appears as false patterns or jagged edges (moiré) that do not exist in the original scene. This occurs because the sensor cannot capture the rapid changes in the signal between adjacent pixels.
Nyquist Criterion
Information theory dictates that the sampling density must be at least twice the highest frequency present in the image. When a system violates this rule, spatial aliasing folds high-frequency details back into lower frequencies. The result is a distorted representation that cannot be corrected by software after the image is captured.
An Optical Filter
An optical low-pass filter removes the details that the pixels are too far apart to resolve. This controlled reduction in sharpness prevents spatial aliasing by ensuring that the input signal stays within the capture capability of the detector. While this lowers the absolute resolution, it improves the overall quality by removing artifacts.
Pixel Density
High-density sensors reduce the chance of spatial aliasing by pushing the sampling limit higher than the typical details in the subject. In production environments, choosing the wrong sensor for a fine-textured object leads to false rejects during automated inspection. The cost of calling a design ready before testing for these artifacts is a significant loss in manufacturing throughput and inspection reliability.