Development of Alternative Filters to Enhance the Quality of Digital Images

In the process of a digital image using computational and mathematical analysis, Discrete Fourier Transforms are commonly used to obtain frequency representations of the corresponding images. Various filters are then designed and applied to obtain frequency-related information. Isolating low-frequency components or high-frequency components is followed to smooth out or detect specific areas in an image. In this paper, a few alternative types of mathematical filters, such as the Gaussian filter, Boxcar filter, and trigonometric filter, were employed to produce images with better or different quality and to compare the processed images with one another. First, as the normal process does, the numerical information on the frequency domain was converted into the information on an image domain using the Fourier Transformation. In this process, different types of modified filters were employed and showed their distinct features and output images. A non-conventional method was tested, transforming the information on the image domain into a frequency space. During the presented process, a few filters turned out to be effective at reducing noise while preserving important image details due to their weighted averaging. However, the results showed some sensitive outcomes depending on using different constants in their formula of filters.