Honey adulteration is a common practice that affects food quality and sale prices, and certifying the origin of the honey using non-destructive methods is critical. Guindo Santo and Quillay are fundamental for the honey production of Biobío and the Ñuble region in Chile. Furthermore, Guindo Santo only exists in this area of the world. Therefore, certifying honey of this species is crucial for beekeeper communities—mostly natives—to give them advantages and competitiveness in the global market. To solve this necessity, we present a system for detecting adulterated endemic honey that combines different artificial intelligence networks with a confocal optical microscope and a tunable optical filter for hyperspectral data acquisition. Honey samples artificially adulterated with syrups at concentrations undetectable to the naked eye were used for validating different artificial intelligence models. Comparing Linear discriminant analysis (LDA), Support vector machine (SVM), and Neural Network (NN), we reach the best average accuracy value with SVM of 93% for all classes in both kinds of honey. We hope these results will be the starting point of a method for honey certification in Chile in an automated way and with high precision.

A reference-free image index to jointly assess infrared-imaging fixed-pattern-noise and blur artifacts is proposed in this work. The proposed index is based on tuned-spatial-domain filtering, which works by combining two Laplace operators to simultaneously quantify the global infrared-imaging fixed-pattern-noise and the global or local blur artifacts. The index effectiveness is demonstrated by two task-based image-quality assessments to determine the focused and fixed-pattern-noise free images from sequences captured with both a mid-wave-infrared microscope system and a long-wave-infrared plenoptic system. The index quantitative limits are shown on numerical computations over synthetic corrupted images as well as real black-body radiator calibrated infrared images with representative simulated fixed-pattern noise, from six well known infrared focal plane arrays transducer technologies, along with artificial blur added using real infrared imaging system point spread functions.

A three-dimensional point spread function experimental estimation method based on the system’s focal plane array spatial local impulse response of a mid-wave infrared microscope is presented. The method uses several out-of-focus two-dimensional point spread function planes to achieve a single three-dimensional point spread function of the whole microscope’s optical spreading, expanding the limits of infrared optical technology by one dimension. This technique includes stages of image acquisition, nonuniformity correction, filtering, and multi-planar reconstruction steps, and its effectiveness is demonstrated on biological sample image restoration by means of a multi-planar refocusing application.

The present work proposes a method for the 3-D full-field focusing for microscopic infrared (IR) imagery. It is based on the partial analysis of Point Spread Function (PSF) for a confined volumetric universe of vision in a microscopic IR system. The ability of the algorithm to compensate for localized blur is demonstrated using two different real MWIR microscopic video sequences, which were captured from two microscopic living organisms using a Janos-Sofradir MWIR microscopy setup. The performance of the proposed algorithm is assessed on real and simulated infrared data by computing the root mean-square error and the roughness-laplacian pattern, which was specifically developed for the present work.

A novel method, to our knowledge, for monitoring weightlifting exercises based on infrared imaging is proposed in this work. For the infrared workout weightlifting recorded scenes, radiometry and artificial intelligence were employed for in-scene temperature and biomechanical athletes’ body parts position mapping. Our method was effective in monitoring muscle exertion during high-performance athletic exercises, as evidenced by the results obtained from real athletic datasets. The method generates a color-labeled sequence of thermal images and reports on body part positions, which can be used by judges and trainers to guide athletes toward safer and more efficient practices.