## 3 Results and Discussion
On the other hand, the EPI factor of the restored image is larger than that of the denoised image, which may be occurred due to over smoothing during the denoising process. Moreover, the sharpness of the degraded image is lower than that of the reference image. It is almost same effect as blurring when performing Laplacian filtering when the contours contain the noise component in image. It will be led to the low EPI value. Based on a comprehensive analysis of the PSNR, COV, and EPI results, we deduce that the restored image using the proposed method achieves the appropriate balance between the noise reduction and sharpness preservation. In addition, by performing a quantitative evaluation for each image restoration method in the simulated Lena image, we demonstrated that the proposed NSCT algorithm can effectively preserve the image edge information and simultaneously reduce the noise level efficiently. Figure 7a shows the difference images compared to the reference image of degraded image (left), denoised image (middle), and restored image (right) and these enlarged images. The error of difference image between the restored image and the reference image is significantly less than that of other images, qualitatively. Moreover, Figure 7b shows the histogram bar graph and MSE results of them. The histogram plot of the difference image between restored image and reference image trends to be lower than that of other images, and MSE results of the difference image of restored image are about 1.27 and 1.02 times lower, compared to that of other images. These results indicated that the restored image using the proposed method accurately removed the noise and blurring components from the degraded image so that the restored image is close to the reference image. Figure 8a shows the results of applying the image quality improvement methods to the light microscopy image of the mandible tissue of mouse. The acquired light microscopy images consisted of the tooth, PDL, and bone tissue from top to bottom and the enlarged images for arrow areas in Figure 8a are shown in Figure 8b-d. From the visual evaluation of the resulting image, it was confirmed that the sharpness of the deblurred image was improved compared with that of the degraded image in all areas of the tooth, PDL, and bone. Here, the deblurred image was generated by applying the previously \(l_{0}\)-norm-based blind deconvolution method [49] to compare the results of the proposed method. Noise amplification in the deblurred image occurred significantly in all areas, and the noise level of the image could be reduced using the denoising method. However, there were cell types in the PDL tissue that were not well observed in the denoised image; we were able to overcome this disadvantage by using the proposed NSCT algorithm (Figure 8c). Figure 6: Evaluated simulation results for the (**a**) COV (from box ROI\({}_{1}\) in Figure 5), (**b**) PSNR, and (**c**) EPI values of the reference, degraded, denoised, and restored images according to the RGB components. Figure 8: (**a**) Experiment results for the degraded image, deblurred image from the degraded image, denoised image using the degraded image, and restored (proposed) image using the proposed NSCT algorithm. (**b–d**) are enlarged images of the tooth, PDL, and bone tissue areas, respectively (black arrows in (**a**)). The red arrow in (**c**) clearly shows the usefulness of the proposed algorithm. Figure 7: (**a**) The difference images compared to the reference image of degraded image (**left**), denoised image (**middle**), and restored image (**right**) and (**b**) the bar graphs of counts with respect to the intensity. Figure 9 shows the quantitative evaluation results for the light microscopic images of the mandible tissue of male mice in terms of the COV. The COV result graph was calculated for ROI\({}_{1}\) in Figure 8a. Similar to the simulation analysis, the COV was analyzed by dividing the R, G, and B colors by the channel in the real light microscopic image.