## 4 Discussion  
Conventional light microscopy is limited by the achievable image quality, in terms of the resolution and noise performance, owing to fundamental factors. The CLSM imaging system has superior transverse resolution in the direction parallel to the X and Y-axes and improved longitudinal resolution in the direction parallel to the Z-axis on the specimen, compared to a conventional microscope with the same objective lens at the same wavelength [17]. Even in a CLSM imaging system with improved resolution, noise removal is essential and must be efficiently improved to facilitate the early detection of various lesions. In this study, a TV-based noise reduction algorithm was developed to achieve improved noise removal, facilitating the diagnosis of various lesions with improved efficiency. The TV noise reduction algorithm had the lowest NNPS value (approximately \(10^{-6}\) mm\({}^{2}\)) in the spatial frequency range, which was \(10^{4}\) times lower than those of the median and Wiener filters (Figure 4). Therefore, this confirmed that the proposed algorithm could achieve a considerably greater degree of noise reduction/removal and provide improved noise characteristics. In addition, the NNPS through random selection was smoother. In the case of the proposed algorithm, the NNPS value decreased rapidly in the spatial frequency range of approximately 0.2-1.0 lp/mm and subsequently decreased gradually until reaching the Nyquist frequency (Figure 4). The CNR of the proposed algorithm was approximately 1.86, 1.77, and 1.53 times higher than that of the noisy image, median filter, and Wiener filter, respectively (Figure 5). The COV of the proposed algorithm was approximately 1.90, 1.40, and 1.29 times better than that of the noisy image, median filter, and Wiener filter, respectively (Figure 5). [PERSON] et al., reported on the applicability of conventional filtering methods using a non-linear filter [18], whereas [PERSON] et al., suggested an intensity correction method with mean filtering following contrast limiting adaptive histogram equalization for CLSM images [19; 20]. However, while non-linear filters have been used since long, these filters have the disadvantage of a low denoising efficiency. In this study, for the first time, an iterative TV noise reduction algorithm has been applied to CLSM images to improve the denoising efficiency. Figure 5: (**a**) CNR and (**b**) COV results (over ROI A and ROI B shown in Figure 2) obtained for the noisy image, median filter, Wiener filter, and proposed TV noise reduction algorithm applied to the confocal laser scanning micrograph of the tooth specimen. The quantitative evaluation results of the noise reduction methods applied to the CLSM imaging system reveal that the designed algorithm achieved excellent image quality. The noise in the CLSM image was more efficiently reduced, in comparison with the conventional filters, owing to the use of denoising models that reject Gaussian and Poisson noises and a smoothing function. The improved results reflect the efficiency of the proposed algorithm for noise reduction by using TV to model the image intensity function, solving the constrained optimization problem using Lagrange multipliers and regularizing the coefficient functions. The results verify that the designed algorithm can help reduce noise in medical tooth specimen images captured with CLSM. To compare the performances in terms of the noise and spatial resolution, the modulation transfer function (MTF) was calculated owing to the trade-off between the noise and spatial resolution. The MTFs corresponding to the different image processing algorithms used for noise reduction were measured. The blind deconvolution method, i.e., the deconvolution method to identify the unknown \(PSF(x,y)\) from microscopy images, was employed.