## 3 Results and Discussion
In particular, it is common to use a regulation prior because it is highly affected  
Figure 11: The gradient magnitude values of degraded image, deblurred image, denoised image, and restored image in Figure 8d. Figure 10: NIQE and BRISQUE results for the experimentally acquired image results using Figure 8a. by artifacts such as noise. Representative priorities include the normalized sparsity-based image prior [54], approximate \(l_{0}\)-norm-based image prior [55], and reweighted \(l_{2}\)-norm-based image prior [56]. These approaches derived improved results; however, unnatural images were generated, and staircase or cartooned artifacts occurred in the case of the \(l_{2}\)-norm-based image prior [57; 58]. The Bi-\(l_{0}\)-\(l_{2}\)-norm prior strategy is an example in which the improved resolution prevents artifacts; however, noise amplification cannot be suppressed [59]. [PERSON] and [PERSON] [60] introduced an image restoration method using deblurring after BM3D denoising. This is a different approach compared to previous studies, showing higher PSNR and structural similarity index (SSIM) [61] values than existing methods. However, unnatural results such as cartooned artifacts are difficult to use in light microscopic image processing. We are continuously performing research and development activities to improve microscopic images [57; 59] and implement the proposed method to address existing problems. Denoising was performed after separating the subbands where noise was mainly distributed through the MRA using NST, and information loss was minimized by obtaining high-resolution subband images without artifacts through \(l_{1}\)-norm-based deblurring. Light microscopy images are also widely used to identify post-mortem intervals, which collectively refer to the phenomena appearing after the death of an animal. [PERSON] et al. [62] observed cellular changes in light microscopy images of oral tissues during the post-mortem interval. They analyzed the ecological changes in various oral tissues; however, the importance of acquiring more precise and stable images in observing the connective tissue was mentioned. It will be possible to determine the post-mortem interval using light microscopy images of oral tissues more accurately after improving the image quality using the proposed algorithm. Furthermore, based on our results, periodontitis, neoplastic growth, or communicable diseases could be analyzed more accurately. Despite its outstanding results, the proposed method has some limitations in terms of the accurate noise level estimation, the remaining low-frequency nose and computational time. Having a large and number of block is very important for predicting the noise levels. This is crucial for performing rank correlation to extract blocks that contain only noise. However, we have not been able to demonstrate exactly how the set size of blocks in optical microscopy images leads to errors and biases in predicting the noise level. It is planned to investigate the accuracy of noise level prediction for block size through statistical analysis based on a large number of optical microscopy images. Although the proposed algorithm focused on high-frequency subbands, noise remained in the low-frequency subband image. In addition, low-amplitude noise affects image quality but is not easy to remove. When the proposed method is applied to low-frequency bands, the overall image quality deteriorates due to distortion caused by excessive deblurring. Recently, various methods have been proposed to overcome this problem, such as removing noise in the low-frequency area using a white noise filter [63] and removing low-amplitude noise using block matching and 4D filters [64]. We expected that these methods will be alternatives that can overcome the limitations of this study. Furthermore, because repeated image processing is performed on numerous high-frequency subbands, considerable computational time is required.