Establishment of hybridized focus measure functions as a universal method for autofocusing

Abstract

Exact focusing is essential for any automatic image capturing system. Performances of focus measure functions (FMFs) used for autofocusing are sensitive to image contents and imaging systems. Therefore, identification of universal FMF assumes a lot of significance. Eight FMFs were hybridized in pairs of two and implemented simultaneously on a single stack to calculate the hybrid focus measure. In total, 28 hybrid FMFs (HFMFs) and eight FMFs were implemented on stacks of images from three different imaging modalities. Performance of FMFs was found to the best at 50% region sampling. Accuracy, focus error, and false maxima were calculated to evaluate the performance of each FMF. Nineteen HFMFs provided >90% accuracy. Image distortion (noise, contrast, saturation, illumination, etc.) was performed to evaluate robustness of HFMFs. Hybrid of tenengrad variance and steerable filter-based (VGRnSFB) FMFs was identified as the most robust and accurate function with an accuracy of ≥90% and a relatively lower focus error and false maxima rate. Sharpness of focus curve of VGRnSFB along with eight individual FMFs was also computed to determine the efficacy of HFMF for the optimization process. VGRnSFB HFMF may be implemented for automated capturing of an image for any imaging system.

An algorithm selection methodology for automated focusing in optical microscopy

“…Secondly, all the images studied were taken in a bright-field microscopy setting, where there are some algorithms with better performance, as there are for fluorescence microscopy (Osibote et al, 2010;Shah et al, 2017). Therefore, it is likely that the same algorithms stand out in all tissues, being always in the top of the ranking.…”

“…Although many algorithms are available, not all of them are suited for a given application. In fact, the optimal algorithm for a certain use varies with the sample and its content (Shah et al, 2017;Yousefi et al, 2011). This is why it is expected that different tissues and magnifications will demand the use of different algorithms, as these parameters directly affect the images' content, along with the fact that conventional microscopy and fluorescence microscopy have different features and the best algorithms vary from one to the other.…”

Parallel implementations to accelerate the autofocus process in microscopy applications

“…1 This mechanism, used in passive systems, is based on the analysis of the highfrequency content of a sequence of images acquired at different focal positions and the same field of view (FOV). 2 In recent years, several algorithms have been proposed to determine the best focused image from a microscopy image stack. As discussed by various authors, [3][4][5][6][7] the performance of an autofocus function (AF) depends on the microscopy modality and the image content, which has been classified as high-, medium-, and low-density background.…”