## 3 Results and Discussion  
Figure 5 shows examples of the reference image (left top), degraded image (right top), denoised image (left bottom) from the degraded image, and restored (proposed) image (right bottom) using the proposed NST-based multiresolution analysis. The restored (proposed) image result exhibits a superior image quality with regard to sharpness and noise level compared to those of the degraded and denoised images. Figure 6 shows the quantitative evaluation results for the COV, PSNR, and EPI metrics of the simulated Lena images. To separately analyze the colors of the three channels observed in the acquired image, the quantitative evaluation results and average values for red (R), green (G), and blue (B) were derived. In all the quantitative evaluation results, the R, G, and B data showed similar trends. The COV results increased in the following order: reference, denoised, restored, and degraded images (Figure 6a). The average COV values of the restored image obtained using the proposed NST algorithm and the denoised image were approximately 0.237 and 0.180, respectively. When the proposed NST algorithm was applied to the obtained Lena image, the COV deteriorated by approximately 1.31 times compared to the image with noise reduction alone. However, we confirmed that the COV of the image after applying the proposed algorithm improved by approximately 2.44 times compared to the degraded image without image processing. The analyzed PSNR values were lowest in the degraded image and highest in the restored image (Figure 6b). The average PSNR values of the restored image obtained with the proposed NST algorithm and the denoised image were approximately 38.71 and 27.98, respectively. When the proposed NST algorithm was applied to the obtained Lena image, we confirmed that the PSNR improved by 1.44 and 1.38 times compared with the degraded and denoised images, respectively. The evaluated EPI values were the highest in the restored image and lowest in the denoised image (Figure 6c). The average EPI values of the restored image obtained using the proposed NST algorithm and degraded image were approximately 0.758 and 0.573, respectively. When the proposed NST algorithm was applied to the obtained Lena image, we confirmed that the EPI results were improved by 1.32 and 1.58 times compared to the degraded and denoised images, respectively. These results for the simulated Lena image indicate that the proposed NST algorithm is useful in terms of noise improvement and edge region preservation. Referring to Figure 6a, for COV, which can evaluate the ratio  
Figure 5: Simulation results for the reference image, degraded image with added blur and noise, denoised image from the degraded image, and restored (proposed) image using the proposed NST-based multiresolution analysis. of noise and signal simultaneously, the best result can only be derived from the denoised image, except for the reference Lena image. However, the part in which the COV result improved by approximately 2.44 times compared to the degraded image in the Lena image after applying the NSCT algorithm means that the denoising performance of the proposed method is within an achievable range. In particular, the best result was obtained from the image to which the NSCT algorithm was applied in terms of the PSNR evaluation result, which shows the degree of image quality loss as the maximum value of the input data and the mean square error (Figure 6b). This means that the NSCT algorithm has excellent overall image restoration performance. In addition, EPI, which indicates the degree of image edge preservation, showed the best value when using the NSCT algorithm, similar to the PSNR result (Figure 6c). Note that, the COV value can be high not only when the noise is removed, but also when the edge component is smoothing. In the case of Figure 6a, the COV value of the restored image is expected to be higher than that of the denoised image due to increase the variation of signal during the deconvolution process.