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Methods used for Bitewing X-ray
|
| (Lai & Lin, 2008) |
Adaptive local contrast stretching is used to make the tooth region smoother after that, adaptive morphological enhancement is applied to improve the texture values. |
| (Prajapati, Desai & Modi, 2012) |
A median filter is used to eradicate picture impulse noise. |
| (Mahoor & Abdel-Mottaleb, 2004; Zhou & Abdel-Mottaleb, 2005; Huang et al., 2012) |
Top hat and bottom hat filters are applied where the teeth become brightened, and the bone and shadow regions obscured. |
| (Pushparaj, Gurunathan & Arumugam, 2013) |
Butterworth high pass filter used with a homomorphic filter. In which homomorphic filter compensates the effect of non-uniform illumination. |
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Methods used for Periapical X-ray
|
| (Harandi & Pourghassem, 2011) |
Histogram equalization and noise reduction using wavelets, and also make use of spatial filters like Laplacian filter. |
| (Lin, Huang & Huang, 2012) |
Average filter with 25 * 25 mask then histogram equalization is used. |
| (Nuansanong, Kiattisin & Leelasantitham, 2014) |
Gaussian spatial filter with kernel size 5 * 5 and sigma value 1.4 is fixed. |
| (Lin et al., 2014) |
Enhancement is done by combining adaptive power law transformation, local singularity, and bilateral filter. |
| (Rad et al., 2015) |
Median filtering is applied to enhance the images |
| (Purnama et al., 2015) |
Contrast stretching used to improve the X-ray quality so that it can be easily interpreted and examined correctly |
| (Jain & Chauhan, 2017) |
Gaussian filtering employed to make a more smoothed gradient nearby the edges also helps in reducing noise. |
| (Obuchowicz Rafałand Nurzynska et al., 2018) |
Histogram equalization (HEQ) and a statistical dominance algorithm (SDA) are initiated. |
| (Singh & Agarwal, 2018) |
Median filtering is used to lower noise, and an unsharp marking filter is used to enhance the high-frequency component. |
| (Datta, Chaki & Modak, 2019) |
Local averaging is used to eliminate noisy features. |
| (Kumar, Bhadauria & Singh, 2020) |
The guided filter is applied with a window size of 3 * 3 and is cast-off towards calculating output pixel size. |
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Methods used for Panoramic X-rays
|
| (Frejlichowski & Wanat, 2011) |
Some basic filters are added to select pyramid layers, including sharpening filter and contrast adjustment before image recomposition. |
| (Vijayakumari et al., 2012) |
Block analysis and contrast stretching applied. |
| (Pushparaj et al., 2013) |
A combination of the Butterworth bandpass filter and the homomorphic filter is used to enhance the edges and illumination. |
| (Razali et al., 2014) |
Canny edge detection is applied, where the gaussian filter is used to eliminate the noise. |
| (Banu et al., 2014) |
Image inverse and contrast stretching procedures have been used to identify the region of interest. |
| (Amer & Aqel, 2015) |
Contrast enhancement with intensity transformations is used to improve the segmentation procedure. |
| (Poonsri et al., 2016) |
Image enhancement using adaptive thresholding (Bradley & Roth, 2007). |
| (Veena Divya, Jatti & Revan Joshi, 2016) |
The image contrast is balanced to enhance the picture’s appearance and to visualize the cyst or tumor. |
| (Zak et al., 2017) |
A combination of top hat/bottom hat filter and adaptive power-law transformation(APLT) is used to enhance images. |
| (Alsmadi, 2018) |
Speckle noise is reduced by using a median filter. |
| (Divya et al., 2019) |
Negative transformation applied and caries identified by using the difference of contrast improved Image and image negative. |
| (Banday & Mir, 2019) |
Adaptive histogram equalization and median filtering are combinedly applied. |
| (Fariza et al., 2019) |
Dental X-ray image is processed using CLAHE, and gamma correction is done to improve the contrast. |
| (Avuçlu & Bacsçiftçi, 2020) |
Median softening filter applied after contrast stretching. |
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Methods used for hybrid dataset
|
| (Said et al., 2006) |
Internal noise is reduced by closing top-hat transformation, which is described by subtracting the picture from its morphological closure. |
| (Tuan, Ngan & Son, 2016) |
Background noise is minimized using a Gaussian filter; then, a Gaussian(DoG) filter is used to measure the gradient along the x and y-axis. |
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Methods used for color images
|
| (Ghaedi et al., 2014) |
A contrast enhancement focused on the histogram is introduced to the gray-level Image. |
| (Datta & Chaki, 2015a) |
Denoising is done by using a wiener filter. |
| (Datta & Chaki, 2015b) |
A Wiener filter is applied to eliminate the blurring effect and additive noise. |
| (Berdouses et al., 2015) |
Gray level transformation performed. |
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Methods used for CBCT & CT
|
| (Benyó et al., 2009) |
Image with high-frequency noise are enhanced by applying a median filter |
| (Ji, Ong & Foong, 2014) |
Initially, the intensity range was adjusted, followed by Gaussian filtering with a standard deviation to suppress noise. |
