Image Enhancement by Unsharp Mask Filtering Based on Detrending Method - Kim, Jeong-Hwan; Lee, Kang-Hwi; Lee, Jeong-Whan; Kim, Kyeong-Seop (International Journal of Engineering and Technology, 2018)
Background/Objectives: Unsharp Masking (UM) technique has been widely used to enhance high-frequency details such as edges by subtracting its local blurriness involved in pre-fixed low-pass filter mask operations and adding high-frequency weighting elements toÂ original image. However, UM process has a major drawback: it estimates only local blurriness by performing convolution operation involved with the pixels in the limited spatial scope. With this aim, we present a new UM method by detrending analysis where overall blurriness in a given image is estimated in terms of a slow and non-stationary global tendency that is resultant of merging adjacent local trends.Methods/Statistical analysis: In our study, global non-stationary trend was estimated by applying detrending method that was originally developed based on smoothness priors approach to estimate slow and non-stationary trend in Heart Rate Variability (HRV) analysis. In our study, non-stationary trend was resolved by merging subsequent local trends due to the limitations in inverse-matrix computations and this global tendency was interpreted as non-stationary part for applying detrending analysis.Findings: Our test results reveal that the suggested UM approach can efficiently estimate the global trend of an image and subsequently it can be utilized to improve the sharpness and contrast of an image.Improvements/Applications: In this study, zigzag-scanning algorithm was used to convert two-dimensional image data into one-dimensional vector form to estimate non-stationary trend of an image based on detrending analysis. In the future study, it might need to explore the role of specific scanning-order how this factor affects the performance of estimating local blurriness of an image.Â