# A Novel Guided Box Filter Based on Hybrid Optimization for Medical Image Denoising
## 4 Results and Discussion
### Result Analysis
Similarly, Figure 10 shows the performance of the proposed HCPSO-based GDB filter on varying decimation sizes. For the CHASEDB1 dataset, PSNR, SSIM, and FSIM are the maximum for the decimation 2 \(\times\) 2., i.e., 37.34, 0.951, and 0.9241, respectively. For the MRI  
Figure 9: Performance of proposed HCPSO-based novel GDB filter for varying noise variance in terms of (**a**) PSNR, (**b**) SSIM, and (**c**) FSIM. dataset, PSNR, SSIM, and FSIM are the maximum for the decimation \(2\times 2\)., i.e., 34.608, 0.946, and 0.9355, respectively. For the ultrasound dataset, PSNR, SSIM, and FSIM are maximum for the decimation \(2\times 2\), i.e., 37.26, 0.957, and 0.9335, respectively. Given these results, the best result was obtained at a smaller size of the decimation box filter. For reliable and robust comparison among bio-inspired optimization-based digital filters, all input images were taken same on the same execution platform. Figure 11 shows the performance of the proposed HCPSO-based GDB filter on varying input image sizes with different datasets, CHSEDB1, MRI, and ultrasound. The image size varied from 128 \(\times\) 128, 192 \(\times\) 192, 256 \(\times\) 256, and 512 \(\times\) 512. PSNR, SSIM, and FSIM are  
Figure 10: Performance of proposed HCPSO-based GDB filter for varying decimation in terms of (**a**) PSNR, (**b**) SSIM, and (**c**) FSIM. the maximum for the 512 \(\times\) 512 image size, i.e., 37.135, 0.940, and 0.906, respectively, for the CHASEDB1 dataset. PSNR, SSIM, and FSIM are the maximum for the 512 \(\times\) 512 image size, i.e., 35.964, 0.950, and 0.943, respectively, for the MRI dataset. PSNR, SSIM, and FSIM are the maximum for the 512 \(\times\) 512 image size, i.e., 37.306, 0.952, and 0.924, respectively, for the ultrasound dataset. Figure 12 shows the performance of the proposed HCPSO-based GDB filter on varying types of noise, such as Gaussian, Poisson, salt and pepper, and speckle with different datasets, including CHSEDB1, MRI, and ultrasound. The image size is taken as 192 \(\times\) 192. PSNR, SSIM, and FSIM are the maximum for Poisson noise. Figure 11: Performance of proposed HCPSO-based GDB filter for varying input image sizes in terms of (**a**) PSNR, (**b**) SSIM, and (**c**) FSIM. Figure 13 presents the time complexity analysis of the proposed HCPSO model for different noises. The average computational complexity of the model is ~1 s, which is quite diminished. Figure 14 presents the performance analysis of digital filters designed for spatial domain filters for images. In terms of the comparative state-of-the-art, the most popular models are considered, such as the Deep Convolutional Neural Network method (DNCNN) [32], adaptive cuckoo search optimization (AD-CSO) [33], adaptive modified cuckoo search optimization (AD-MCSO) [33], bilateral filter optimization using cuckoo search optimization (BF-CSO) [34], deep image prior combined with regularization by denoising (DeepRED) [35], enhanced GWO optimization [13], unsupervised deep learning (DNN) [36], ant colony optimization (ACO) [37], Bi-dimensional Empirical Mode Decomposition (BEMD) [38], a bilateral filter with a genetic algorithm (BF-GA) [39], the linear  
Figure 12: Performance of proposed HCPSO-based GDB filter on different noises in terms of (**a**) PSNR, (**b**) SSIM, and (**c**) FSIM. minimum mean square error method for principal component analysis (LMMSE-PCA) [40]. These existing state-of-the-art models are compared with the proposed HCPSO on approximate PSNR and SSIM values. The comparative graphs are also presented in Figure 14. Given the above graphs, it is clear that the proposed HCPSO provides better results in terms of PSNR and SSIM compared to the existing state-of-the-art models, as the PSNR is the quantitative performance evaluation parameter, while SSIM is the qualitative performance evaluation parameter. Therefore, it can be concluded that the designed HCPSO preserves the quality of the image after denoising. Table 6 presents the comparative state of the art for the noise-handling capability of algorithms.