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Figure from:Investigation of Superresolution using Phase based Image Matching with Function Fitting

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fitting function has a higher PSNR values compared with Keren registration, even for images with less texture. PSNR results, images that contain less texture

Figure 4 fitting function has a higher PSNR values compared with Keren registration, even for images with less texture. PSNR results, images that contain less texture

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In this case F(k,,k2) and G(k,,k2) denote Discrete Fourier Transforms (DFT) from spatial domain f(n,,n2) and g(n1,n2), n,and nz are elemen index in spatial domain atf(n,,n2), ky = —M,, wy My, ky = —Mp, My; Ar(ky,k2) and AG(kk>) is the amplitude component; e/9Fuk2and e/%ckuk2) are the phase component.
In this case F(k,,k2) and G(k,,k2) denote Discrete Fourier Transforms (DFT) from spatial domain f(n,,n2) and g(n1,n2), n,and nz are elemen index in spatial domain atf(n,,n2), ky = —M,, wy My, ky = —Mp, My; Ar(ky,k2) and AG(kk>) is the amplitude component; e/9Fuk2and e/%ckuk2) are the phase component.
Figure 2 shows an illustration of the POCs in the frequency domain. On the right side of the circuit depicted low resolution image (LRI), which through the process to define the image preprocessing is important and will be used in the reconstruction process, if all the image used then this process can be by passed. LRI to be used in the process of reconstruction in the Fourier transform into LRS. On the left is defined initial high-resolution image (SRI) obtained from the LRI which has been scaled or from a simple interpolation methods as nearest, bilinear, or bicubic. SRI in the re sample to adjust to the LRI, in this case when the rotation the LRI experience on SRI is also subject to rotation. SRI then transformed with a Fourier transform image super resolution overlay (SRO). SRO later in the projection in order to set constraints are defined using the LRS.
Figure 2 shows an illustration of the POCs in the frequency domain. On the right side of the circuit depicted low resolution image (LRI), which through the process to define the image preprocessing is important and will be used in the reconstruction process, if all the image used then this process can be by passed. LRI to be used in the process of reconstruction in the Fourier transform into LRS. On the left is defined initial high-resolution image (SRI) obtained from the LRI which has been scaled or from a simple interpolation methods as nearest, bilinear, or bicubic. SRI in the re sample to adjust to the LRI, in this case when the rotation the LRI experience on SRI is also subject to rotation. SRI then transformed with a Fourier transform image super resolution overlay (SRO). SRO later in the projection in order to set constraints are defined using the LRS.
This experiment was performed on an object that contains lots of textures and also ones with less texture. The purpose of this experiment was to get the best performance of composite between PBIM and Keren registration with SANC and POCs reconstruction algorithm POCs Test Images (a)image that contains lots of textures (b)image that contains less of textures
This experiment was performed on an object that contains lots of textures and also ones with less texture. The purpose of this experiment was to get the best performance of composite between PBIM and Keren registration with SANC and POCs reconstruction algorithm POCs Test Images (a)image that contains lots of textures (b)image that contains less of textures
Results of several experiments are discussed in the following section. Table 1 shows the PSNR values of image registration using composite of PBIM and reconstruction using two reconstruction algorithms, POCs algorithm and SANC algorithm. There are eleven experiment results from composite of two registration, PBIM and Keren registration algorithm with variant LR that contain lots of texture image. Table 1 also shows the highest average of PSNR that achieved in composite between PBIM registration with POCs algorithm and has PSNR average value of 32.558. Composite between PBIM registration and SANC algorithm reconstruction has an average PSNR value of 32.16065, while Keren registration has lower PSNR value than the PBIM registration, for both construction algorithms. It shows that the registration PBIM is better than keren registration. Figure 5 presents a graph of the relationship between PSNR value and the number of reference image used for reconstruction. Image used in this experiment is an image that contain lots of texture.
Results of several experiments are discussed in the following section. Table 1 shows the PSNR values of image registration using composite of PBIM and reconstruction using two reconstruction algorithms, POCs algorithm and SANC algorithm. There are eleven experiment results from composite of two registration, PBIM and Keren registration algorithm with variant LR that contain lots of texture image. Table 1 also shows the highest average of PSNR that achieved in composite between PBIM registration with POCs algorithm and has PSNR average value of 32.558. Composite between PBIM registration and SANC algorithm reconstruction has an average PSNR value of 32.16065, while Keren registration has lower PSNR value than the PBIM registration, for both construction algorithms. It shows that the registration PBIM is better than keren registration. Figure 5 presents a graph of the relationship between PSNR value and the number of reference image used for reconstruction. Image used in this experiment is an image that contain lots of texture.
value and PBIM and various of construction algorithms. Figure 8 shows e between _ the SR results for image with less texture. Figure 7 shows the relationship between PSNR value and PBIM and various of construction algorithms. Figt various numbers of reference images, and composite between _ the SR results for image with less texture.
value and PBIM and various of construction algorithms. Figure 8 shows e between _ the SR results for image with less texture. Figure 7 shows the relationship between PSNR value and PBIM and various of construction algorithms. Figt various numbers of reference images, and composite between _ the SR results for image with less texture.