Abstract
The modulus of the gradient of the color planes (MGC) is implemented to transform multichannel information to a grayscale image. This digital technique is used in two applications: (a) focus measurements during autofocusing (AF) process and (b) extending the depth of field (EDoF) by means of multifocus image fusion. In the first case, the MGC procedure is based on an edge detection technique and is implemented in over 15 focus metrics that are typically handled in digital microscopy. The MGC approach is tested on color images of histological sections for the selection of in-focus images. An appealing attribute of all the AF metrics working in the MGC space is their monotonic behavior even up to a magnification of 100×. An advantage of the MGC method is its computational simplicity and inherent parallelism. In the second application, a multifocus image fusion algorithm based on the MGC approach has been implemented on graphics processing units (GPUs). The resulting fused images are evaluated using a nonreference image quality metric. The proposed fusion method reveals a high-quality image independently of faulty illumination during the image acquisition. Finally, the three-dimensional visualization of the in-focus image is shown.
Highlights
Automatic autofocusing (AF) in digital microscopy is highly dependent on the sample topography variability and its color distribution
This procedure transforms the multichannel information to a grayscale image, which is used for (a) focus measurements during the AF process and (b) for extending the depth of field (DOF) in the framework of digital microscopy applications
The AF experimental results of this work demonstrate the effectiveness of the MGC method when it is applied to several z-stacks of images
Summary
Automatic autofocusing (AF) in digital microscopy is highly dependent on the sample topography variability and its color distribution. As stated by Qu et al.,[1] different focus criterion functions perform quite differently even for the same sample. The majority of these methods have been addressed to study AF in the context of monochromatic frames.[2,3,4,5] many works have been published that present a comparative evaluation of the performance of these kinds of AF techniques.[6,7,8] Some research has determined that the best AF metric is based on the Brenner function;[2] other research gives priority to the variance,[9] Vollath-4,10–12 or the sum-modified-Laplacian,[13] among other methods. A waveletbased technique for converting multichannel (e.g., color) data to a single channel by principal components analysis has been reported for this task;[17] it is computationally intense
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