Coherent noise reduction of phase images in digital holographic microscopy based on the adaptive anisotropic diffusion

Abstract

The suppression of coherent noise can produce higher-quality reconstructed images in digital holographic microscopy. A robust and effective phase coherent noise denoising algorithm is proposed in this paper that combines the anisotropic diffusion equation and the phase quality map. In order to accurately identify the noise and signal pixels, we introduce the phase quality map and edge detection to quantify the quality of the pixel information. In addition, a synthetic diffusion function is established to control the speed of the anisotropic diffusion process based on the quality coefficient. Several experiments have been carried out to validate the effectiveness of the proposed algorithm for coherent noise reduction. The results demonstrate that the proposed algorithm can reduce coherent noise and preserve edge details well.

Applying Speckle Noise Suppression to Refractive Indices Change Detection in Porous Silicon Microarrays

“…Previous works have introduced some improvements to these classical algorithms and have achieved good results [27,28,29,30,31,32]. Recently, scholars have also proposed advanced algorithms, such as probability-based nonlocal means filtering [33], numerical multilook and 3D block-matching filtering [34], adaptive wavelet threshold processing [35], and adaptive anisotropic diffusion [36]. In this paper, a variety of filtering algorithms were experimentally analyzed.…”

“…Five advanced algorithms previously proposed by other scholars, as implemented in MATLAB, were considered for comparison in terms of calculating the average gray value and SI: Probability-based nonlocal means filtering [33], numerical multilook and 3D block-matching filtering [34], adaptive wavelet threshold processing [35], adaptive anisotropic diffusion [36], and Lee filtering. The average gray values (AGL) were processed to two decimal places and the SI was processed to four decimal places.…”

“…The proposed algorithm is effective for eliminating speckles that obscure the real image, can suppress the influence of speckle noise, and can restore the gray value of the image. We compared the results of our proposed algorithm to the results of five other advanced algorithms, including probability-based nonlocal mean filtering [33], numerical multilook and 3D block matching filtering [34], adaptive wavelet threshold processing [35], adaptive anisotropic diffusion [36], and Lee filtering. We found that the adaptive median filtering algorithm did not achieve the desired effect in removing real speckles.…”

Dynamic Granularity Matrix Space Based Adaptive Edge Detection Method for Structured Light Stripes

“…Furthermore, the binary fringe is more robust against noise. According to many research conclusions [7][8][9][10][11], binary fringe patterns can reduce the impact of the nonlinear gamma effect of projection, thereby reducing the system error and improving the measurement accuracy. In addition, the structured light measurement method based on line laser is also widely used in engineering field [5].…”

“…In addition, the structured light measurement method based on line laser is also widely used in engineering field [5]. For binary fringe patterns and line laser stripes, a key step is to distinguish and extract the binary black-and-white stripes from the ambiguous zone [5,[9][10][11], in which image segmentation is the precondition for accurate analysis of stripe phase information. In real applications, the surfaces of objects are textured, and the radiation intensity of the scene is relatively high, leading to reduced contrast and distorted image.…”