Structured-illumination reflectance imaging for the detection of defects in fruit: Analysis of resolution, contrast and depth-resolving features

Authors: LU, YUZHEN - Michigan State University; Lu, Renfu

Submitted to: Biosystems Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/16/2019
Publication Date: 2/4/2019
Citation: Lu, Y., Lu, R. 2019. Structured-illumination reflectance imaging for the detection of defects in fruit: Analysis of resolution, contrast and depth-resolving features. Biosystems Engineering. 180:1-15. https://doi.org/10.1016/j.biosystemseng.2019.01.014.
DOI: https://doi.org/10.1016/j.biosystemseng.2019.01.014

Interpretive Summary: Structured-illumination reflectance imaging (SIRI) provides a new means for acquiring images from samples by using the illumination of special patterns, compared to uniform illumination that is commonly used in conventional imaging inspection systems. The technique has shown superior performance in detecting defects such as subsurface bruising in apples. However, the capability of SIRI for defect detection is closely related to its image resolution and contrast and depth-resolving features. This study was therefore aimed at providing theoretical and experimental analyses of these features for SIRI, in order to gain qualitative and quantitative knowledge of the technique’s potential for detecting surface and subsurface defects of fruit and other food products. A mathematical formulation was derived for describing the formation of acquired images by SIRI and its derived images. The ability of SIRI for providing enhanced image resolution and contrast and depth-resolving features was experimentally evaluated using standard optical targets and apple fruit. It was found that the alternating component images derived from SIRI contributed to enhanced image resolution and contrast as well as its depth-resolving capability. As the spatial frequency of illumination patterns increased, the depth-resolving capability of SIRI decreased. The effectiveness of AC in defect detection of apples largely depended on such factors as defect type, fruit surface morphology (and variety) and spatial frequency of illumination. SIRI has limited depth-resolving capabilities of imaging subsurface tissues, and the technique is thus useful for enhanced detection of surface and subsurface defects of thin-skinned fruits like apple.

Technical Abstract: Structured-illumination reflectance imaging (SIRI) provides a new means for fruit defect detection by using structured illumination for sample imaging, compared to uniform illumination that is commonly used in conventional imaging inspection systems. The capability of SIRI for defect detection, however, mainly depends on its image resolution and contrast and depth-resolving features. This study was therefore aimed at providing theoretical and experimental analyses of these features for SIRI, so as to evaluate its potential for detecting surface and subsurface defects of fruit and other food products. The image formation in SIRI was first modeled in terms of the optical transfer functions, which led to a general image demodulation methodology and also provided insights into the features of direct component (DC) and amplitude component (AC) images that were demodulated from the original SIRI pattern images. A set of experiments were performed on standard optical targets and apple samples by using illumination patterns over a wide range of spatial frequencies of 0.01 to 1.0 cycles/mm to examine the features of SIRI. The imaging process acted as a low-pass filter, which, coupled with the sample absorption and scattering, accounted for the resolution and contrast loss of resulting images. As the hallmark of SIRI, AC images possessed enhanced resolution and contrast, which could be explained by the synergistic effect of frequency shifting in the Fourier domain and suppressed light scattering in the sample, and also a depth-resolved ability by varying the spatial frequency of illumination patterns. However, the depth of tissue interrogation by the AC images decreased as the spatial frequency of illumination patterns increased. The effectiveness of AC in defect detection of apples depended on such factors as defect type, fruit surface morphology (thus variety) and spatial frequency of illumination. Without resorting to an inverse computational approach, SIRI should be positioned as a subsurface-surface modality for detecting thin-skinned fruits like apple. Further research is needed to explore the full potential of SIRI in detecting subsurface and surface defects of fruits and other food products.