# A Deconvolutional Deblurring Algorithm Based on Dual-Channel Images
## 5 Experimental Results  
To verify the restoration effect of the proposed algorithm, three groups of experiments were used for verification. The Group-1 experiments were simulation experiments with simulation data; the Group-2 experiments involved real-shot image restoration with space-invariant images; and Group-3 were real-shot experiments with space-variant images. The three groups of experiments verified the practicability of the algorithm by comparing the restoration results of the Levin Library, sample images provided by other literature, and our images. Among them, the simulated images sets were quantitatively analyzed through a variety of indicators, and the real-shot images sets were generally selected to use the method of visual observation to evaluate the restoration quality. Moreover, the results from [11] (code download on [[http://zoi.utia.cas.cz/download_toolbox](http://zoi.utia.cas.cz/download_toolbox)]([http://zoi.utia.cas.cz/download_toolbox](http://zoi.utia.cas.cz/download_toolbox)) (accessed on 28 May 2020) with permission) and [14] (code used with GNU) were calculated using the copyright permission when the software was downloaded. Group-4 used space-variance blurred images of the target plate to test the recoverability of this method. All programs were running on the student version of MATLAB 2017 under the licensed Windows 11 platform. The CPU was an Intel Core i7 8750H, and 32G memory with Intel 750 SSD. Peak signal-to-noise ratio (PSNR, unit: dB) and structural similarity index (SSIM) were calculated using MATLAB's functions, with default parameters. Group 1: In this group, the non-space-variant blurred images of the Levin image library were used to compare and test the restoration ability of the algorithm for non-space-variant blurred images. This group mainly verified the estimation ability of the algorithm to the kernel, so we chose to compare all 32 results from the total of four original images and eight kernels. The comparisons were obtained from the studies by [PERSON] et al. [14; 21; 22; 23]. The codes of those papers were downloaded from their official websites on GitHub, with a GPL or GNU open-source license. The comparison method also used the PSNR and SSIMto measure the results from the two algorithms. The total images, and kernels are shown in Figure 4, and the results are shown in Figure 5.  
Furthermore, the results of [21; 22; 23] were provided by their official GitHub site. The results were not obtained by the authors of this paper running the program on this machine, except running times or special declarations. Figure 4a shows the original images and Figure 4b the self-designed simulation blur kernel. Figure 5 shows all the results obtained when simulations were performed in two-by-two teams in the order of images and blur kernels. It can be seen that the proposed algorithm performed well overall, in terms of the PSNR and SSIM. Discussion: As shown in Figure 5, with the PSNR, this algorithm not only produced a result up to 35+, but the mean and the concentration of the distribution interval of the PSNR of this algorithm were higher than the other algorithms. Moreover, the lower bound of this algorithm in the PSNR was higher than the others. The proposed algorithm also performed well in for the SSIM. It led, both in terms of the maximum and mean values, and the lower bound of the interval. This shows that the recovery results of this method are more stable overall, with less variation in the results for images with different blurring patterns. Moreover, with stability  
Figure 4: The Levin library; (**a**) the original images from the Levin library; (**b**) kernels. Figure 5: The result; Left is the SSIM; Right is PSNR. The red line is the average. The blue box is the main area of concentration for the median 50% of the data. The black horizontal line indicates the highest value in the median region. The red cross indicates the highest value for the entire set. If there is no red cross, then the highest value is at the black horizontal line above.