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

Abstract

Structured-illumination reflectance imaging (SIRI), compared to conventional uniform illumination based modalities, provides a new means for defect detection of fruit. 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 modelled 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 0.01–1.0 cycles mm−1 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 defects that are on or close to the surface of fruits, especially thin-skinned fruits like apple. Further research is therefore needed to explore the full potential of SIRI in defect detection of fruits and other food products.