Based on porous silicon (PSi) microarray images, we propose a new method called the phagocytosis algorithm (PGY) for removing the influence of speckle noise on image gray values. In a theoretical analysis, speckle noise of different intensities is added to images, and a suitable denoising method is developed to restore the image gray level. This method can be used to reduce the influence of speckle noise on the gray values of PSi microarray images to improve the accuracy of detection and increase detection sensitivity. In experiments, the method is applied to detect refractive index changes in PSi microcavity images, and a good linear relationship between the gray level change and the refractive index change is obtained. In addition, the algorithm is applied to a PSi microarray image, and good results are obtained.

The shortcoming of the coherence of digital holographic light sources leads to the generation of holographic reconstruction speckle noise, which seriously affects the quality of holographic reconstruction. Although many advanced algorithms for reducing speckle noise in digital holography (DH) have achieved good results, in the process of processing image speckle noise, the details of the image are blurred, and the information relating to the image texture is lost. Therefore, we propose a new algorithm, which combines automatic GrabCut and guided filtering to solve the problem of blurred image details in filtering reconstruction. The new algorithm is applied to the problem of holographic speckle removal for the first time. In this paper, the new algorithm not only achieves quasi-noise-free (approximate the noise-free state) DH reconstruction but also realizes the retention of details in the image. The quality of the resulting holographic reconstruction is comparable to that achieved by incoherent technology, which exceeds the current level of DH and produces a good visual effect. INDEX TERMS Holography, guided filtering, automatic GrabCut, noise.

The gray value method can be used to detect gray value changes of each unit almost parallel to the surface image of PSi (porous silicon) microarrays and indirectly measure the refractive index changes of each unit. However, the speckles of different noise intensities produced by lasers on a porous silicon surface have different effects on the gray value of the measured image. This results in inaccurate results of refractive index changes obtained from the change in gray value. Therefore, it is very important to reduce the influence of speckle noise on measurement results. In this paper, a new algorithm based on the concepts of probability-based nonlocal-means filtering (PNLM), gradient operator, and median filtering is proposed for gray value restoration of porous silicon microarray images. A good linear relationship between gray value change and refractive index change is obtained, which can reduce the influence of speckle noise on the gray value of the PSi microarray image, improving detection accuracy. This means the method based on gray value change detection can be applied to the biological detection of PSi microarray arrays.

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