Point in the image is searched. The padded image need to be cropped together with the vanishing point as the center and also the maximum length in the vanishing point towards the edge of image as the side length. Thirdly, the CGDM on the cropped image is calculated. The depth map in the original image is obtained by cutting the CGDM of the cropped image. 2.3. DNQX disodium salt Description close-up Images If no convergence point is detected in the image, or many convergence points are detected, the image are going to be classified as a close-up image, rather than a viewpoint view image. For any close-up image, the CGDM is often found utilizing occlusion [41]. Regions that contain fewer edges generally represent that they’re farther away. The spatial connection of a series of objects with many depths could be easily determined by counting the amount of edges. The neighborhood edge histogram [40] is employed to calculate the CGDM. As shown in Figure 2, the edges of the 2D image are very first extracted by Canny algorithm. Then, the image in the edges is divided into 5 5 blocks. The amount of edges Nij in every single block is counted. Blocks where Nij is larger than the typical (Nav ) are defined as the major blocks. The total number of key blocks is M, while the amount of edges in each primary block is denoted by N1 , N2 , . . . , NM . From a series of simulations, the reliable CGDM for every main block is proved to become a circle with a depth gradient. The center in the circle locates in the center on the corresponding block. The pixel values with the center and the circumference are assigned as 255 and 0, respectively. Meanwhile, the pixel value decreases evenly in the center towards the circumference. The radius with the circle is obtained by traversal comparisons, and an optimized radius will be the half the length (or width) of the image. The CGDM for the whole close-up image is obtained by fusing collectively the depth maps from the principal blocks. If Di could be the CGDM of your lth key block, the fused CGDM Df could be expressed as: Df =Appl. Sci. 2021, 11,Nii =1 Mi =Di MNi5 of(6)Figure 2. Calculation the CGDM for a close-up image. Figure 2. Calculation ofof the CGDM for a close-up image.3. Calculation and Reconstructions of CGHs 3.1. Calculation of CGHs A layer-based holographic algorithm [42] is employed to calculate the CGHs. BecauseFigure two. Calculation of the CGDM for a close-up image.3. Calculation and Reconstructions of CGHsAppl. Sci. 2021, 11,3.1. Calculation of CGHs5 ofA layer-based holographic algorithm [42] is employed to calculate the CGHs. an 8-bit CGDM is employed, the 3D model obtained by 2D-to-3D rendering is sl 3. Calculation and Reconstructions of CGHs 256 parallel layers. A random phase r (x, y) is superposed on each layer to sim three.1. Calculation of CGHs diffusive effect with the object surface. The complicated amplitude distribution on t A layer-based holographic algorithm [42] is employed to calculate the CGHs. Since graphic plane Ecom (x, y) is calculated as follows: an 8-bit CGDM is employed, the 3D model obtained by 2D-to-3D rendering is sliced into256 parallel layers. A random phase r (x, y) is superposed on each layer to simulate the 255 two diffusive effect of x, yobject surface. FTcomplex y ) exp ir ( x, y ) expon 2 zholographic Ecom ( the ) = FT -1 The U l ( x, amplitude distribution i the l 1 – ( u ) plane Ecom (x, y) is calculated as follows: l ={}- ( v )Ecom ( x, y) =l == ( cos ) – ( ( cos FT-1 FTUl ( x, y) exp[ir ( x, y)] expu i2zl 1 – /(u,)two v = v)2 /) /(7)where FT represents the Etiocholanolone Epigenetic Reader Domain Fourier trans.