Autofocusing Algorithm for Pixel-Super-Resolved Lensfree On-Chip Microscopy

Abstract

In recent years, lensfree on-chip microscopy has developed into a promising and powerful computational optical microscopy technique that allows for wide-field, high-throughput microscopic imaging without using any lenses. However, due to the limited pixel size of the state-of-the-art image sensors, lens-free on-chip microscopy generally suffers from low imaging resolution, which is far from enough to meet the current demand for high-resolution microscopy. Many pixel super-resolution techniques have been developed to solve or at least partially solve this problem by acquiring a series of low-resolution holograms with multiple lateral sub-pixel shifting or axial distances. However, the prerequisite of these pixel super-resolution techniques is that the propagation distance of each low-resolution hologram can be obtained precisely, which faces two major challenges. On the one hand, the captured hologram is inherent pixelated and of low resolution, making it difficult to determine the focal plane by evaluating the image sharpness accurately. On the other hand, the twin-image is superimposed on the backpropagated raw hologram, further exacerbating the difficulties in accurate focal plane determination. In this study, we proposed a high-precision autofocusing algorithm for multi-height pixel-super-resolved lensfree on-chip microscopy. Our approach consists of two major steps: individual preliminary estimation and global precise estimation. First, an improved critical function that combines differential critical function and frequency domain critical function is proposed to obtain the preliminary focus distances of different holograms. Then, the precise focus distances can be determined by further evaluating the global offset of the averaged, low-noise reconstruction from all backpropagated holograms with preliminary focus distances. Simulations and experimental results verified the validity and effectiveness of the proposed algorithm.

Enhancing Autofocus in Non-Mydriatic Fundus Photography: A Fast and Robust Approach with Adaptive Window and Path-Optimized Search

“…Unlike static focus scenarios [27][28][29], NMFP requires rapid execution within relatively ideal imaging conditions to mitigate the impact of low-quality fundus images. In this study, a high-performance autofocus search should have the following: (i) fast convergence; (ii) a strong ability to suppress local extrema.…”

“…To expediently find this maximum, the preference leans towards employing greedy local searches (such as hill climbing [20] and golden section search [18]) instead of stable global searches (such as exhaustive search [26] and function fitting [27]). Recent research introduced mechanisms, including coarse-to-fine [28] and adaptive step-size [29], to constrain the scanning range, thereby speeding up the convergence and enhancing robustness. However, the above methods are only designed for the ideal NMFP imaging condition.…”

A Drop-in, Focus-Extending Phase Mask Simplifies Microscopic and Microfluidic Imaging Systems for Cost-Effective Point-of-Care Diagnostics

“…Furthermore, in the case of flow-based systems, they still require flow-focusing, as the depth of focus within a single image is still limited. By contrast, lensless and other holography-based imaging methods that recover the sample’s optical phase can utilize complex numerical reconstructions to digitally refocus samples across a wide range of depths, − removing the need for focusing. However, such systems, because of their reliance on a coherent interference process, cannot be used with fluorescence imaging.…”