Table 2.

Brief overview of various pre-processing methods to remove noise and enhance the image quality

Image pre-processing method	Description
Mean filter [40]	The mean filter replaces each pixel value in an image with the mean value of its neighbouring pixels, including itself
Median filter [40]	The median filter replaces each pixel value in an image with the median of neighbouring pixels, including itself
Wiener filter [41]	The Wiener filter is based on statistical properties to filter out the noise that has corrupted the original signal
Bilateral filter [42]	It is a non-linear, edge-preserving, and noise-reducing smoothing filter. It does the spatial averaging without smoothing edges
Gaussian filter [43]	It is linear smoothing filter where the filter (kernel) weights are chosen according to the shape of the Gaussian function
Unsharp masking [44]	The unsharp masking technique sharpens an image by calculating the difference between orignal and its blurred version. It increases the contrast of small details in the magnified texture
Histogram equalisation [44]	It is a technique of adjusting image intensities to enhance contrast. This is achieved by stretching out the most frequent intensities, helping low contrast regions to achieve high contrast. The histogram equalisation method helps to improve the global contrast of the image
Adaptive histogram equalisation [44]	It is adaptive method that computes several histograms, each corresponding to a distinct region of the image, and uses them to redistribute the intensity values of the image. Adaptive histogram equalisation is suitable for improving local contrast in the image
CLAHE [45]	Compared to histogram equalisation and adaptive histogram equalisation that are global contrast enhancement methods, the Contrast Limited Adaptive Histogram Equalisation (CLAHE) performs local contrast enhancement. This has been widely adopted in improving lower contrast in ultrasound imaging