## 3 Results and Discussion
Consequently, the highest and lowest COV values were derived for the deblurred and denoised images, respectively. The average COV values of the restored experimental image obtained using the proposed NSCT algorithm and the denoised experimental image were approximately 0.496 and 0.259, respectively. In particular, we demonstrated that the COV result for the restored image improved by approximately 2.52 times compared to that of the deblurred image. Even in the experimental results, the COV of the denoised image was more improved than that of the restored image; however, a no-reference-based evaluation method was used to analyze the image quality considering the overall sharpness. Figure 10 shows the quantitative evaluation results for the no-reference-based evaluation parameters, including NIQE and BRISQUE, of the light microscopy images of the mandible tissue of real male mice. For the deblurred, denoised, and restored images, the obtained NIQE results yielded values of 3.52, 5.20, and 3.88 for the deblurred, denoised, and restored images, respectively. Compared to the denoised image, we confirmed that the NIQE result for the restored image improved by approximately 1.34 times. In addition, the obtained BRISQUE results yielded values of 16.09, 38.57, and 20.08 for the deblurred, denoised, and restored images, respectively. Compared with the denoised image, we confirmed that the BRISQUE result for the restored image improved by approximately 1.92 times. As it is difficult to quantitatively evaluate the spatial resolution in a real experimental image, the NIQE and BRISQUE parameters were used to evaluate the overall image quality without using a reference. In the two no-reference-based evaluation results, it was demonstrated that the restored image can properly reduce noise and blurring in the light microscopic image compared to the denoised image. In the COV result, which can quantitatively evaluate the noise level, a superior value was shown in the denoised image; however, edge information was lost owing to excessive blurring in the acquired image. In addition, the NIQE and BRISQUE results based on natural scene statistics exhibited similar tendencies, confirming the usefulness of the two evaluation parameters. However, the PSNR and EPI results for the degraded image obtained in the simulation study and the no-reference-based evaluation of the real experimental image have different trends; therefore, we considered that additional verification is required. Figure 9: Experimental COV results for the area indicated by box ROI\({}_{1}\) in Figure 8a. To quantitatively verify the improvement in sharpness, we calculated the GM value for degraded image, deblurred image, denoised image, and restored image using the proposed method. Figure 11 shows the GM values for each channel (e.g., red, green, and blue) and average for each image. The GM average values of degraded, deblurred, denoised, and restored image were 1.90, 5.82, 1.66, and 3.44, respectively. The deblurred image, which has the highest sharpness, has the highest GM value, followed by the restored image, degraded image, and denoised image. These results shows that the resolution of the deblurred and restored images increases by 3.06 times and 1.81 times, respectively, compared to that of the degraded image. Although the calculated GM value is higher for deblurred image than restored image, it is important to note that deblurred images have amplified noise compared to restored images, which reduces the overall image quality. Thus, we expect that various analyzes using GM metric will be possible after the development of new microscope image processing algorithms in the future. Noise presents a significant difficulty when attempting to improve the sharpness of microscopic images. To overcome this problem, finding solutions using the model in Equation (3) while minimizing the effect of noise, improved sharpness results are derived.