Improved three-dimensional reconstruction algorithm from a multifocus microscopic image sequence based on a nonsubsampled wavelet transform.

In the multifocus microscopic image measurement method, the distortion of the three-dimensional (3D) reconstruction model has always been an important factor affecting the measurement result. In spatial domains, the focus measure algorithm is based on the gradient change of the pixel point to determine the degree of focus of the pixel. So it will be difficult to accurately extract the focus of the pixel in the areas where color difference is not obvious, resulting in 3D model distortion. According to the optical principle, the high-frequency coefficients of the clear image are larger than the high-frequency coefficients of the blurred image. Based on this characteristic, this paper proposes a new multifocus microscopic image 3D reconstruction algorithm using a nonsubsampled wavelet transform (NSWT). The NSWT does not consider the downsampling in wavelet decomposition and has translational invariance. Therefore, the wavelet transform value of each pixel can be calculated in the image, so the high-frequency coefficient of each pixel can be obtained; then the convolution calculation is performed on the high-frequency coefficients of the pixel points in the fixed window as the focus measure value of the pixel point. Compared with the traditional algorithm, the algorithm proposed in this paper can show better unimodal and antinoise performance on the focusing measure curve. In this paper, the reconstruction of the experimental object is Alicona standard block triangular and semicylindrical. The proposed algorithm and the traditional algorithm for comprehensive measure use the root mean square error, peak signal to noise ratio, and correlation coefficient as the measure index. The experimental results and comparative analysis prove the correctness of the proposed algorithm and enable more accurate reconstruction of 3D models based on multifocus microscopic images.

Three-dimensional surface microtopography recovery from a multifocus image sequence using an omnidirectional modified Laplacian operator with adaptive window size.

Optical microscopy enables the observation of highly magnified objects and material structures on microsurfaces, but it can only acquire 2D images. In order to observe areal features more accurately and intuitively, 3D surface microtopography recovery has been applied to form a 3D surface model of an object from its 2D image sequence. In the 3D reconstruction of the focus evaluation operator, we have the gray variance operator, the gray-scale difference absolute sum operator, the Roberts gradient operator, the Tenengrad gradient operator, the improved Laplace operator, etc. There are two problems with these operators: one is that there is no difference between (x,y) and the gray scale of the pixel in the diagonal direction in the field and the other is that the window size of the focus evaluation operator is fixed, e.g., 3×3, 5×5, etc. Thus, the size of the window for each pixel in the image is the same, and the small window may not cover enough field information while being vulnerable to noise. Large windows can cover more information, but they may result in a smoothing phenomenon, which affects the accuracy of the model. Different pixels around the field have different pixel colors when the size of the window is not the same. Therefore, this paper proposes a modified omnidirectional Laplacian operator with an adaptive window to automatically adjust the size of the window according to the color difference within the window. This also takes into consideration the pixels in the diagonal direction. In addition, very comprehensive verification experiments proved the conclusions.