Digital holographic microscopy (DHM) is a topographic measurement technique that permits full-field, nondestructive, dynamic, quantitative amplitude, and phase-contrast imaging. The technique may realize the lateral resolution with submicron scale and the longitudinal resolution with subnanometer scale, respectively. Improving imaging quality has always been the research focus in DHM since it has a direct effect on the precise topographic measurement. In this paper, the recent progress on phase aberration compensation and coherent noise suppression is reviewed. Included in this review are the hologram spectrum’s centering judgment methods of side band in tilt phase error compensation, the physical and numerical compensation methods in phase aberration compensation, and the single-shot digital process methods in coherent noise suppression. The summaries and analyses for these approaches can contribute to improving the imaging quality and reducing the measurement error of DHM, which will further promote the wider applications of DHM in the topographic measurement fields, such as biology and micro-electro mechanical systems.

The adaptive spatial filtering method is commonly adopted to extract the $+1$ term spectrum in digital holography for real-time dynamic analysis. However, the typical filtering method is not satisfactory for automatic analysis, because the reset of the filtering window is needed to extract the area of the $+1$ term spectrum. Therefore, an adaptive spatial filtering method based on region growing and the characteristic of the spectrum separation is proposed. Its filtering window is automatically formed by region growing. The key parameters, including threshold and seed point, are set by the intensity distribution of the hologram spectrum. Then the adaptive filtering extracting the $+1$ term spectrum is realized by multiplying the hologram spectrum by the filtering window. Compared to the typical filtering method, the experimental results of a microhole array and a phase step show that the proposed method has better adaptability and a higher precision. Moreover, the applicability of this method for different uses is also demonstrated by experiments with a microhole array and a phase step.

The telecentric arrangement in digital holographic microscopy (DHM), considered to be a pure-physical compensation for defocus aberration introduced by microscope objective (MO), shows shift-invariant behavior. Its optical arrangement requires precise adjustment of the distance between MO aperture stop and collimated lens. However, it is difficult to measure and quantify the distance even by monitoring the spatial frequency spectrum of recorded hologram in the absence of object. Thus the misalignment results in the residual defocus aberration in the telecentric arrangement. The total aberrations compensation for misalignment of telecentric arrangement in DHM is presented, in which a posteriori surface fitting method based on Zernike polynomials is performed to eliminate the residual defocus aberration as well as other primary aberrations. The approach reduces the difficulty in precise alignment of the telecentric arrangement and decreases the measurement error caused by aberrations in construction. Three-dimensional retrieval of the height for micro-hole arrays with high-spatial-frequency content demonstrates the feasibility of the method.

Coal gangue image recognition is a critical technology for achieving automatic separation in coal processing, characterized by its rapid, environmentally friendly, and energy-saving nature. However, the response characteristics of coal and gangue vary greatly under different illuminance conditions, which poses challenges to the stability of feature extraction and recognition, especially when strict illuminance requirements are necessary. This leads to fluctuating coal gangue recognition accuracy in industrial environments. To address these issues and improve the accuracy and stability of image recognition under variable illuminance conditions, we propose a novel coal gangue recognition method based on laser speckle images. Firstly, we studied the inter-class separability and intra-class compactness of the collected laser speckle images of coal and gangue by extracting gray and texture features from the laser speckle images, and analyzed the performance of laser speckle images in representing the differences between coal and gangue minerals. Subsequently, coal gangue recognition was achieved using an SVM classifier based on the extracted features from the laser speckle images. The fusion feature approach achieved a recognition accuracy of 94.4%, providing further evidence of the feasibility of this method. Lastly, we conducted a comparative experiment between natural images and laser speckle images for coal gangue recognition using the same features. The average accuracy of coal gangue laser speckle image recognition under various lighting conditions is 96.7%, with a standard deviation of the recognition accuracy of 1.7%. This significantly surpasses the recognition accuracy obtained from natural coal and gangue images. The results showed that the proposed laser speckle image features can facilitate more stable coal gangue recognition with illumination factors, providing a new, reliable method for achieving accurate classification of coal and gangue in the industrial environment of mines.