###### Abstract  
The confocal laser scanning microscopy (CLSM) system has been widely used to analyze early carious lesions with fluorescent ligands in dental imaging. This system can be used to examine the physiological condition of cellular colonization in the tooth structure. However, the undesirable noise in CLSM images hinders accurate activity assessment of early carious lesions. To address this limitation, a total variation (TV)-based noise reduction algorithm with good edge preservation was developed, and its applicability to medical tooth specimen images obtained with CLSM was verified. To evaluate the imaging performance, the proposed algorithm was compared with conventional filtering methods in terms of the normalized noise power spectrum, contrast-to-noise ratio, and coefficient of variation. The results indicate that the proposed algorithm achieved better noise performance and fine-detail preservation, in comparison with the conventional methods.  
Keywords:total variation noise reduction algorithm; confocal laser scanning microscopy; medical diagnosis; caries activity; evaluation of image performance +
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spatial depth control is feasible through optical slicing. [PERSON] et al., employed an interference confocal microscope with a simultaneous phase-shifter device to achieve an axial depth resolution of 1 nm [3]. CLSM can be used to image hard tissues, which are more difficult to capture compared to soft tissues. This approach has been employed in the field of dentistry [4].  
CLSM has been used to identify the activity of early carious lesions [5; 6; 7]. [PERSON] et al. observed a higher concentration of bacteria in active lesions than in arrested lesions [8]. However, simple microbiological tests (for, e.g., colony forming unit assays) are not useful for differentiating between active and arrested dental caries. Using CLSM to accurately observe live bacteria within the early carious lesions on the enamel can contribute to the development of diagnostic criteria for early caries based on the lesion activity. In particular, [PERSON] et al., used CLSM to measure the bactericidal effect against a biofilm in the tooth from 20 to 590 s and confirmed that the antibacterial effect improved when Protect Bond was used [9]. In addition, [PERSON] et al., verified that a copper-silver alloy can achieve low bacterial contamination using a tailor-made CLSM to visualize the killing of bacterial biofilms [10].  
The resolution of a CLSM image is poor owing to the inevitable noise generated while processing the contrast. Image noise can limit the early detection of carious lesions in tooth specimen images. In addition, the edge information is low owing to the limited amount of light emanating from the focused beam. Total variation (TV)-based approaches, which were first introduced by [PERSON] et al., in 1992, have been applied to medical systems to reduce image noise while preserving edge information [11]. Many studies have been conducted to evaluate the applicability of TV-based noise reduction algorithms to various medical imaging systems [12; 13; 14; 15; 16]. [PERSON] et al., applied an optimization model with a TV-based noise reduction algorithm to a chest computed radiography (CR) X-ray image and reported a sensitivity of approximately 79% [12]. In addition, our previous research studies reported that the TV-based noise reduction algorithm outperformed conventional filters, such as the median and Wiener filters, when applied to computed tomography (CT) and chest X-ray medical imaging systems [13; 15]. However, studies on the applicability of this algorithm to CLSM images are limited. In this context, a TV-based noise reduction algorithm for CLSM images was developed, and its image performance was evaluated in terms of the normalized noise power spectrum (NNPS), contrast-to-noise ratio (CNR), and coefficient of variation (COV).