This work presents an angular and polarization multiplexing technique for synthetic aperture (SA) imaging in digital holographic microscopy by using a pair of phase-type spatial light modulators (SLMs) to achieve resolution enhancement through a single hologram acquisition. Angular multiplexing is applied to adjust the incident wave propagation at different specific deflection angles by modulating a SLM panel as a binary phase grating. The other SLM panel is introduced to control the polarization of the propagating object beams and thus ensure orthogonal polarization between these waves to avoid mutual interference of the conjugated first-order diffracting beams derived from the same phase grating. Experimental results indicate that the spatial resolution and signal-to-noise ratio of the reconstructed image are efficiently improved. Furthermore, an electrically addressed SLM pair creates the possibility to perform a dynamic and precise manipulation for realizing angular-polarization multiplexing SA imaging without the use of mechanical scanning mirrors.

We proposed a complex defect inspection (CDI) technique for quality control of transparent substrates that uses the diffraction characteristics of digital holograms and a machine learning algorithm. A complex pattern diffraction model was built to provide two diffraction criteria, the least separation of confusion and the effective diffraction distance, to extend depth of focus in the effective diffraction regime for numerical reconstruction. On the basis of an analysis of three-dimensional diffraction characteristics of complex images, defect identification was performed to detect and classify defects (cracks, dusts, and watermarks) in transparent substrates using region-based segmentation and a machine learning algorithm. The experimental results indicated that the defect detection performance of the proposed CDI system was recall = 96.3% and precision = 92.8%. Moreover, overall multiclass classification accuracy = 95.3%, resulting in a discrimination area under the receiver operating characteristic curve ( Az ) of 0.96.

This study presents a novel label-free approach for characterizing cell death states, eliminating the need for complex molecular labeling that may yield artificial or ambiguous results due to technical limitations in microscope resolution. The proposed holographic tomography technique offers a label-free avenue for capturing precise three-dimensional (3D) refractive index morphologies of cells and directly analyzing cellular parameters like area, height, volume, and nucleus/cytoplasm ratio within the 3D cellular model. We showcase holographic tomography results illustrating various cell death types and elucidate distinctive refractive index correlations with specific cell morphologies complemented by biochemical assays to verify cell death states. These findings hold promise for advancing in situ single cell state identification and diagnosis applications.

This paper presents an effective three-dimensional (3D) surface reconstruction technique aimed at profiling composite surfaces with both specular and diffuse reflectance. Three-dimensional measurements based on fringe projection techniques perform well on diffuse reflective surfaces; however, when the measurement targets contain both specular and diffuse components, the efficiency of fringe projection decreases. To address this issue, the proposed technique integrates digital holography into the fringe projection setup, enabling the simultaneous capture of both specular and diffuse reflected light in the same optical path for full-field surface profilometry. Experimental results demonstrate that this technique effectively detects and accurately reconstructs the 3D profiles of specular and diffuse reflectance, with fringe analysis providing the absolute phase of composite surfaces. The experiments validate the effectiveness of this technique in the 3D surface measurement of integrated circuit carrier boards with chips exhibiting composite surfaces.

This study proposes a regional fringe analysis (RFA) method to detect the regions of a target object in captured shifted images to improve depth measurement in phase-shifting fringe projection profilometry (PS-FPP). In the RFA method, region-based segmentation is exploited to segment the de-fringed image of a target object, and a multi-level fuzzy-based classification with five presented features is used to analyze and discriminate the regions of an object from the segmented regions, which were associated with explicit fringe information. Then, in the experiment, the performance of the proposed method is tested and evaluated on 26 test cases made of five types of materials. The qualitative and quantitative results demonstrate that the proposed RFA method can effectively detect the desired regions of an object to improve depth measurement in the PS-FPP system.